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Metacarpophalangeal joint patterns based personal identification system
Applied Soft Computing 37 (2015) 288–295
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Applied Soft Computing journal homepage: www.elsevier.com/locate/asoc
Metacarpophalangeal joint patterns based personal identification system Necla Ozkaya ∗ Erciyes University, Engineering Faculty, Computer Engineering Department, 38030 Kayseri, Turkey
a r t i c l e
i n f o
Article history: Received 10 November 2014 Received in revised form 24 January 2015 Accepted 16 May 2015 Available online 4 July 2015 Keywords: Biometrics Metacarpophalangeal joint patterns Recognition Identification Verification
a b s t r a c t This paper introduces a novel approach for identity authentication system based on metacarpophalangeal joint patterns (MJPs). A discriminative common vector (DCV) based method is utilized for feature selection. In the literature, there is no study using whole MJP for identity authentication, exceptionally a work (Ferrer et al., 2005) using the hand knuckle pattern which is some part of the MJP draws the attention as a similar study. The originality of this approach is that: whole MJP is firstly used as a biometric identifier and DCV method is firstly applied for extracting the feature set of MJP. The developed system performs some basic tasks like image acquisition, image pre-processing, feature extraction, matching, and performance evaluation. The feasibility and effectiveness of this approach is rigorously evaluated using the k-fold cross validation technique on two different databases: a publicly available database and a specially established database. The experimental results indicate that the MJPs are very distinctive biometric identifiers and can be securely used in biometric identification and verification systems, DCV method is successfully employed for obtaining the feature set of MJPs and proposed MJP based authentication approach is very successful according to state of the art techniques with a recognition rate of between 95.33% and 100.00%. © 2015 Published by Elsevier B.V.
1. Introduction Biometric technology uses certain physiological characteristics or behavioral traits to the automatic recognition of an identity. It is the most convenient and reliable alternative to satisfy the needs for personal authentication [2,3]. Hand-based biometric techniques also exploit varied internal, external and behavioral features that are unique for an individual, like fingerprints, 3D finger geometry, hand geometry, palm prints, hand veins, finger knuckle patterns, nails patterns, signatures and keystroke dynamics [1–6]. The human hand based recognition systems need very less cooperation from users for enrolment of the data whose capturing is non-intrusive. Good quality samples can be collected by low cost devices [7]. Recently, new algorithms and applications of handbased biometrics have emerged. As these are reliable, low-cost and user-friendly viable solutions to personal authentication, most of these approaches have been well investigated in the literature [8–10]. Hand-based biometric systems also have some advantages over other techniques. These systems include easy and economical data acquisition, suitability for both indoor and outdoor usage
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including use in several bad weather and lighting conditions, the stability of hand features over time and the fact that they are the most user-friendly techniques in the biometrics field [8,10,11]. Although the hand-back surface may be highly useful in identity authentication due to its containing some unique patterns with rich lines, textures and creases, it has attracted very little attention from researchers [12]. The most commonly used and most popular biometric identifier from back-hand surface is finger knuckle print (FKP) [6,8–22]. Since FKP-based studies have shown significant performances as a result of the use of quick algorithms with very high accuracy, FKP recognition is a well-known technology. There are two different publicly available FKP databases for researchers who want to study FKPs [23,24]. A brief summary of FKP-based studies from the biometrics literature is given below. A study aimed at identity authentication using a 3D finger surface was introduced by Woodard and Flynn. In their study, they have developed a surface representation scheme for the middle, ring and index fingers using hand’s 3D range images. The curvature based shape index to represent the fingers’ surface was also used and a Recognition Rate (RR) of between 58% and 98% was obtained [22]. The first studies about the finger-back surface were conducted by Ravikanth and Kumar. They have used various combinations of hand geometry features, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component
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Analysis (ICA). They have obtained an Error Equal Rate (EER) of between 1.94% and 5.81% [21]. The FKP pattern was firstly used in a biometric-based recognition system and introduced as a biometric identifier by Zhang et al. The authors have developed and presented various accomplished studies about FKP recognition. A 2D Gabor filter based FKP recognition method was presented with a 97% RR and 1.09% EER value [19], a feature extraction scheme which combines orientation and magnitude information extracted by Gabor filters was introduced with a 1.51% EER value [20]. They have also used a Band-Limited Phase-Only Correlation based method with an EER of 1.68% for their experiment 1 and an EER of between 0% and 5.5% for their experiment 2 [15]. A feature extraction scheme which combines orientation and magnitude information extracted by Gabor filtering for FKP verification was introduced in the literature [9]. FKP recognition by extracting and assembling local and global FKP features was achieved with a 0.402% EER and 96.80% RR [8]. Kumar and Zhou have developed successful FKP recognition studies. They achieved personal identification using finger knuckle orientation features. A personal identification system via finger images which exploits the orientation features from the random knuckle lines using finite Radon Transform was introduced with 98.6% RR and 1.14% EER [12]. They have also used KnuckleCodes for personal authentication. The enhanced knuckle images were employed to generate KnuckleCodes using localized Radon Transform for FKP-based human identification. They have obtained a 1.08% EER [13]. Kumar and Ravikanth have achieved FKP detection and have used the geometrical features of the finger and FKP patterns for identity authentication with 1.39% EER [11]. Chorás and Kozik have used texture-based knuckle features for personal identification with 1.02% EER [18]. A method applying a Gabor filter to enhance FKP information and a Scale Invariant Feature Transform method to extract the FKP features were introduced by Morales et al. They have used fingers separately in addition to use various combinations of four fingers and obtained highly accurate results from their different experiments [16]. A multi-modal biometric system based on palm print and FKP using a Phase-Correlation Function based matching method was proposed by Meraoumia et al. [10]. DCV based method FKP recognition is also achieved by us recently [6]. Inspired by the findings on FKP, we aimed to determine if the MJP that is the biggest single pattern endowed with highly unique features and original formation on the back-hand surface found on the metacarpophalangeal bones was unique or not. We also studied with the MJP, which contains much more lines and creases than FKP, to establish if it could be used securely for personal authentication. In this study, the details of the experimental results of this investigation are presented. When the hand-based biometric literature was reviewed, no study was found on whole MJP for identity authentication. The most similar approach [1] to our work is introduced by Ferrer et al. They used the hand knuckle pattern (HKP) as a biometric identifier. HKP is some part of the MJP. Their processing procedure of the HKP is also very different from our methodology. There are time consuming pre-processing tasks like selecting the region of interest (ROI), converting the HKP image into the gray level, binarization process with a pixel by pixel thresholding, enhancement, masking, dividing the HKP image into small blocks, applying the Fast Fourier Transform and inverse of it for each blocks [1]. Our feature selection method directly employed the gray level images without any enhancement task. In this work, whole MJP is firstly used as a new biometric identifier, DCV is firstly utilized for get the feature set of the MJPs and eventually a robust biometric system with high accuracy that can be applicable for real-time usage is designed. The scheme of the proposed MJP recognition approach is introduced in Section 2. Our experiments with both the publicly available database and the established database are given in
289
Image Enrolment Image Pre-processing Feature Extraction
MJP database
Matching and Decision Making
Identification
Verification
Person’s identity
Accept or Reject
Test & Evaluation ( RR, FAR, FRR, EER, ROC )
Fig. 1. Working principles of the proposed system.
Section 3. In addition, the results acquired are also appreciated and commentated on in this section. The paper is finished by stating conclusions in Section 4. 2. MJP recognition approach In this study, an identity authentication system using MJP, found on the back of the hand, is designed and presented. Enrolment phase is the first step of the proposed approach. Pre-processing tasks are accomplished after image acquisition. In that phase fixedsize MJP images are semi-automatically obtained from the hand images and converted to gray level format. The feature selection duties are achieved afterwards with the help of DCV method. Kumar and Zhou stated in their paper [12] that because of the non-uniform reflections and a highly curved surface of the finger, FKPs have non-uniform brightness and low contrast. This makes the recognition process a complicated and intractable task. Due to the similar structures, MJPs are crucial patterns with their low contrast and non-uniform brightness. In spite of these problems, our feature selection phase extracts the features without any enhancement tasks and the matching phase also achieves the comparison using the obtained feature vectors with the help of the DCV-based method which was firstly introduced by Cevikalp et al. for face recognition [25]. The original formation of the MJP ensures to produce unique feature vectors for DCV based method. After designing the system, evaluation process should be applied. To establish a fair comparison, two different databases are used. The first one is previously used in the literature [1], and the second is a non-uniform MJP database established and used in this study. It is publicly available now for further research efforts [26]. Experimental results for both databases are very promising with RRs between 95.33% and 100%, and offering excellent potential for reliable personal identification. Fig. 1 shows the working principles of the proposed system. 2.1. Databases The performance analysis is critically important for a biometric system. To demonstrate the performance of the system a proper evaluation process should be applied with suitable databases. For evaluating the proposed system two different databases are used: the first one is the database used in [1]. It contains 20 subjects with eight images for any hand. Therefore, there are a total of
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Fig. 2. A set of hand images for a person from the first database [1].
160 images in the database. A Sony DSC-PS sensor was used to obtain the hand images. Images were captured in JPEG format and measured 2048 × 1536. The distance between the hand and a blackboard is fixed to 12 cm. Three pegs were placed on the board for positioning the hands. The proposed approach achieved 100% recognition accuracy in this uniform database. Fig. 2 shows a set of hand images for a randomly selected person from this database. To point out the superior success of the presented approach a non-uniform database was established and used in this study. For established database, non-uniform means that: it is established in different and changing conditions. Contrary to the first database, there is no specially designed environment like specific imaging system, pegs, fixed distances or settled cameras. The hand images of the established database are from 30 volunteers. 10 images are collected from each hand. Totally, the database consists of 600 hand images which are collected over a period of two weeks, in three phases, with an average interval of about seven days. A digital single-lens reflex camera with 10.2 megapixel DX format CCD is used in the image acquisition process. While taking images, the camera was not set a fixed condition, contrarily, the photos of the hands was taken manually with a variety of environmental conditions like lighting, distance between the camera and the hand, angular positions of the camera and the hand. The size of the hand images is 4608 × 2592. Since it has been created the database over three days and at varying times of the day in
natural light without extra or standard light, there is no uniform illumination in the database. The distance of the camera was randomly changed between 15 cm and 25 cm from the imaging surface. As reported in [9], these factors significantly reduce the performance of a biometric system, and to avoid these corruptive effects, researchers preferred a uniformly illuminated image acquisition device [9]. The hand image set of a randomly selected person from our established database and ROI of the last image are given in Fig. 3. The images from the established database display large variation within the same class on account of distance, angles, shadows, illuminations and pose changes. These corruptive effects make the recognition task much more stringent duty. In that case, for an ordinary method, some pre-processing tasks for MJP images like enhancement, improvement, translation, rotation are unavoidable. However, DCV-based MJP recognition approach was able to distinguish the MJP images without any of these processes; in spite of all these corruptive effects it performs the tasks with high accuracy. 2.2. Pre-processing The proposed work has two pre-processing tasks: a fixed-sized ROIs of MJPs are obtained from the hand images and these ROIs are converted to gray level format. A semi-automatic method is used for obtaining the ROIs. It works as follows: User determines the ROI of the MJP by manually clicking the center of the MJP. After
Fig. 3. A set of hand images for a person from the second database [26], and ROI of the last image.
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291
Fig. 4. ROIs of a randomly selected individual from the non-uniform database.
determination the center point of the ROI, dedicated software separates the fixed sized rectangle ROIs automatically from the hand images. ROIs of a randomly selected individual from the nonuniform database are given in Fig. 4. 2.3. Feature extraction
⊥ = span{˛k |Sw ˛k = 0, k = r + 1, . . ., d, ˛k ∈ Rd }
In Eqs. (4) and (5), d > r and r is the rank of SW . {˛1 · · · ˛d } is an orthonormal set while {˛1 · · · ˛r } is the set of orthonormal eigeni has a unique formation as given in vectors. Every MJP sample xm Eq. (6). i i i = ym + zm xm
In this study, the DCV-based feature extraction method was effectively employed to obtain the discriminative feature sets of MJPs. The DCV is an appearance-based method that was introduced and successfully applied for face recognition by Cevikalp et al. [25]. In this work, it is preferred to use the DCV method as given in that reference. Therefore, a brief explanation of the method and its working principles are borrowed it and given below; more theoretical information about the DCV method, such as the mathematical background, theorems and proofs can be found in that reference. Like other appearance-based approaches, the DCV method handles the images as two-dimensional holistic patterns. In the DCV technique, to get the common vectors within-class scatter matrix of all classes is used. The size of the null space of these matrixes is critically important for the success of the DCV technique. If this size is small, accuracy of the system is expected to be poor. In the DCVbased method, the training set contains C classes each of which are composed of N patterns. In that case, the training set is composed i represents mth sample from the ith class of M = N × C patterns. xm and it is column vector with d-dimensional. Let d > M − C. SW , SB and ST are given as follows in Eqs. (1)–(3). SW =
N C
i xm − i
i xm − i
T
(1)
C
N(i − )(i − )T
(6)
i In Eq. (6), ym
i Pxm
i QQ T xm
i V , zm
i =Q i ∈ V ⊥, Q = ¯ Q¯ T xm ¯ m = Px
= = ∈ [˛1 · · ·˛r ], Q¯ = [˛r+1 · · ·˛d ]. In addition, P is the orthogonal projection operator onto v, while P¯ is the orthogonal projection operator onto ⊥ . For any MJP, the reason for the unique decomposition is that ⊕ ⊥ = Rd . In this i = xi − yi = xi − Pxi . Especially, an case, the aim is to compute zm m m m m orthonormal basis for v obtained from the normalized eigenvectors ˛k regarding to the nonzero eigenvalues of SW . The eigenvectors may be achieved by computing the smaller M × M matrix (AT A)’s eigenvectors. SW = AAT , and A is a d × M matrix given in Eq. (7). In the method, i ) will for same class’s all patterns, the same unique vector (xcom be acquired. The optimal projection vectors are calculated after i obtaining xcom named the common vectors with Eq. (8). In Eq. (9), W is a matrix. The columns of W are the orthonormal optimal projection vectors wk . In Eq. (10), Scom is the scatter matrix of the common vectors and com is the expected value of all common vectors.
1 C A = x11 − 1 · · ·xN − 1 , x12 − 2 · · ·xN − C i xcom
=
i xm
i − QQ T xm
J(Wopt ) =
i=1 m=1
SB =
(5)
(2)
=
i Q¯ Q¯ T xm ,
(7)
m = 1, . . ., N, i = 1, . . ., C
T T arg max W SB W = arg max W ST W W T SW W =0 W T SW W =0 = arg max W T Scom W
(8)
(9)
W
i=1
ST =
N C
i xm −
T
i xm −
= SW + SB
(3)
Scom =
i xcom − com
i xcom − com
T
,
i = 1, . . ., C
(10)
i=1
i=1 m=1
In Eqs. (1)–(3), and i represent the expected value of all patterns and the expected value of the patterns in the ith class, respectively. MJP patterns were projected onto the null space of SW to obtain the optimal projection vectors W and then the projection vectors were obtained by using PCA. To achieve this task, the spanning vectors of the null space of SW must be calculated. This computation is demanding and difficult duty because of the huge dimension of SW . To accomplish this task by simplifying in a significantly lower-dimensional space, the orthogonal complement of the null space of SW is used. v and ⊥ represent the range space of SW and the null space of SW , respectively. v and ⊥ can be given as follows: = span{˛k |SW ˛k = / 0, k = 1, . . ., r, ˛k ∈ Rd }
C
(4)
Then, wk may be determined by an eigenanalysis of Scom that is the d × d matrix (Scom = Acom ATcom ). To find the eigenvectors and the nonzero eigenvalues of Scom , the smaller matrix (ATcom Acom ) sized C × C is used. Acom is the d × C matrix with the form in Eq. (11).
1 C − com · · ·xcom − com Acom = xcom
(11)
If all the common vectors are linearly independent, the C − 1 optimal projection vectors are achieved. If two common vectors are equal, then the same vector is obtained for these two classes, and the system cannot distinguish the classes. For MJP images of the i the feature vector can be achieved using the following ith class xm equation: ˝i =
i i xm , w1 · · · xm , wC−1
T
(12)
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pairs of biometric samples from the same identity [27]. Taking decision is critical step for a biometric system. In the verification mode of the biometric system, the decision is regulated by a threshold value, . Matching procedure produces a similarity measure, T. A comparison is then made T and . If two biometric features produce a T higher than or equal to , system decides that this pair of biometric images belongs to the same person. If this T is lower than , the system infers that these biometric features belong to different identities. FAR is the percentage of impostor pairs whose T is greater than or equal to , and FRR is the percentage of genuine pairs whose T is less than . There is a tradeoff between FRR and FAR that are functions of [27]. In order to make the system more tolerant with corruptive effects, is decreased, then FAR() increases. Similarly, if the system is expected to be more secure, is increased, then FRR() increases. While designing the biometric system it may not be known in advance the particular application for which the system may be used. So it is need to exhibit the system performance at all operating points () by ROC curve that is a plot of FAR against (1-FRR) [27]. ROC is one of the most commonly used evaluation metrics in the biometrics literature. EER represents the error rate at the for which FAR and FRR are equal. In biometric systems, because of the finite number of matched pair, similarity measures are not continuous. So an exact EER point might not be found, and instead of a single value an interval is used. It is commonly preferred more stringent is set to decrease FAR in spite of a rise in FRR for a biometric system [27].
3. Experimental results
Fig. 5. DCV of the MJP given in Fig. 4.
i ,w The projection coefficients of the equation xm k are independent of the sample index m. This independence guarantees 100% recognition accuracy for the train data set. The discriminative common vectors, ˝i , that were given in Eq. (13), are used for the identification process. In the test phase, for classification of the test pattern xtest , the feature vector of xtest is determined by Eq. (14). i (m = 1, . . ., N; i = 1, . . ., C) ˝i = W T xm T
˝test = W xtest
(13) (14)
After comparison with ˝i of each class, the system response will be produced. In that case, the closest discriminative common vector to ˝test is found and used to recognize the test image. The common vector obtained from the MJPs given in Fig. 4 is presented in Fig. 5. 2.4. Performance evaluation The performance of a biometric system is commonly analyzed according to some parameters like Recognition Rate (RR) indicates the accuracy of the biometric system, False Acceptance Rate (FAR) (that is also known as False Match Rate (FMR), Type I error or False Positive Rate), False Rejection Rate (FRR) (that is also called False Non-Match Rate (FNMR), Type II error or False Negative Rate), Equal-Error-Rate (EER) and The Receiver Operating Characteristic (ROC) curve. FAR and FRR are obtained from score distributions called impostor distribution that is the score distribution generated from pairs of biometric samples from different identities and genuine distribution that is the score distribution generated from
A MJP recognition system, which is explained in the previous section, is developed in this study. All the steps of the system were achieved in a proper manner and accurate results are obtained. The first step was to establish a new non-uniform hand image database [26] in addition to the publicly available database [1]. Acquiring the MJP images was from the first system duties. After completing the pre-processing tasks of the images, the MJPs were further examined to get the discriminative common vectors. The next step included the matching and decision-making stages which were performed using an Euclidean distance-based strategy. Accuracy is the most important factor used to judge a biometric-based recognition method. Therefore the test and evaluation steps were applied according to the k-fold cross validation technique on two different databases. For an identity, in the experiment with the first database, 6 MJPs were employed for training and remaining 2 MJPs for testing. In the experiment with the second database, 8 MJPs from the established non-uniform database were employed for the training and the remaining 2 for testing. In the evaluation stages, the training-test samples were selected from the databases according to the 4-fold cross validation and 5-fold cross validation technique, respectively. In cross validation, a set of data was divided into subsets called training set and testing set. In this study, the original sample data set was randomly partitioned into 4 subsamples in the first database [1], and into 5 subsamples in the second database [26]. From these subsamples, a single subsample was separated from others as the validation data for testing the system. The remaining subsamples were used as training data as explained in Ref. [28]. In the experiment with the first database, cross validation process was then repeated 4 times while 5 times in the experiment with the second database. In cross validation it is guaranteed that each of the subsamples was used exactly once as testing data. Then the results from k (k = 4 and k = 5) folds were combined by averaging to produce a single estimation. All patterns were used for both training and testing, and each pattern was used for testing exactly once by means of cross validation. In order to obtain reliable results and show the robustness of the proposed system, the training and
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FMR(t) and FNMR(t) CURVE
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a) FAR_FRR representation and ROC curve for publicly available DB [1] FMR(t) and FNMR(t) CURVE
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c) FAR_FRR representation and ROC curve for LH of established DB [26] Fig. 6. FAR FRR representation and ROC curve of designed system for (a) publicly available DB, (b) RH of established DB and (c) LH of established DB. DB: Database, RH: Right Hand, LH: Left Hand, FAR: False Acceptance Rate, FRR: False Rejection Rate, ROC: The Receiver Operating Characteristic.
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N. Ozkaya / Applied Soft Computing 37 (2015) 288–295 Table 5 EER values of DCV-based method with small-sized databases.
Table 1 Performance comparison between classifiers [1] and present study. Classifier
EER average
HMM [1] SVM [1]
0.096 0.094
DCV [present study]
0.002
Database
Publicly available DB part1 (class size: 10) Publicly available DB part2 (class size: 10) RH of established DB part1 (class size: 15) RH of established DB part2 (class size: 15) LH of established DB part1 (class size: 15) LH of established DB part2 (class size: 15)
EER: Error Equal Rates.
Table 2 Performance evaluation of DCV-based MJP recognition scheme. Database
RR%
Publicly available DB RH of established DB LH of established DB
100.00 95.33 96.66
Table 3 EER values of DCV-based MJP recognition system.
Publicly available DB RH of established DB LH of established DB
FAR
FRR
90
0.0153
0
Between 83 and 95
0
0
95
0.0100
0.0600
89
0.0195
0
90
0.0153
0
92
0.0124
0.0333
DB: Database, RH: Right Hand, LH: Left Hand, : threshold, EER: Error Equal Rates FAR: False Acceptance Rate, FRR: False Rejection Rate.
RR: Recognition Rate, DB: Database, RH: Right Hand, LH: Left Hand.
Database
Error Equal Rates (ERR%)
ERR%
FAR
FRR
89 86 89
0.0020 0.0475 0.0189
0 0.0300 0.0500
DB: Database, RH: Right Hand, LH: Left Hand, : threshold, EER: Error Equal Rates FAR: False Acceptance Rate, FRR: False Rejection Rate.
Table 4 Recognition rates of DCV-based method with small-sized databases. Database
RR%
Publicly available DB part1 (class size: 10) Publicly available DB part2 (class size: 10) RH of established DB part1 (class size: 15) RH of established DB part2 (class size: 15) LH of established DB part1 (class size: 15) LH of established DB part2 (class size: 15)
100.00 100.00 96.67 99.33 98.67 97.33
RR: Recognition Rate, DB: Database, RH: Right Hand, LH: Left Hand.
test procedure was repeated 30 times in each studied case and the RRs were calculated by averaging the errors of all the runs. A performance comparison between the proposed approach and the study given in [1] using the same database is given in Table 1. In the established database experiment, the test and evaluation were performed under different modalities: right hand (RH) and left hand (LH). Table 2 shows both the RRs of the publicly available database and the two types of hands for the established database. Table 3 shows the EER values of the DCV-based MJP recognition scheme for , for both the first database and two types of hands for the established database. It is given both FAR and FRR values corresponding to the that is the threshold value has the minimum FAR-FRR combination. The FAR and FRR representations and ROC curves of the proposed system are presented in Fig. 6(a)–(c). As is mentioned in [25,29], the size of the null space of the within-class scatter matrix is highly effective on the accuracy of the DCV based systems. Thus, the DCV-based recognition approach performs the task properly with small-sized databases. To show the real performance of the DCV-based MJP recognition approach, we also performed experiments using small-sized databases by determining the class size as 15 which was inspired from Ref. [25]. The RR and EER values of the system for small-sized databases were also obtained and are presented in Tables 4 and 5, respectively. Tables 1–5 and Fig. 6 demonstrate that the designed system achieves the tasks perfectly for both the publicly available database [1] and the established database [26]. This robust system with high
accuracy can be securely used as a biometric identification and verification system. It has much more success on small-sized databases. These results also indicate that MJPs can be reliably used to distinguish people from each other in biometric recognition systems. 4. Conclusion In this study, DCV based MJP recognition approach is introduced and a robust biometric system is designed. In the approach, whole MJP is used as a biometric identifier, DCV method is firstly used for feature selection of the MJPs. Proposed system merits to take place in the biometrics literature with the high satisfactory results a RR of 100.00% and an EER of 0.0000–0.0200 in the first database and a recognition rate of between 95.33 and 99.33% and EER of 0.0000–0.0500% in the second database. The obtained results indicate the following findings related to the proposed approach: (i) MJP based identity authentication system is designed. Whole MJP is firstly used as a biometric identifier. (ii) DCV method firstly used for feature extraction of MJPs. (iii) A sensible, robust, reliable biometric system is developed not only for high security applications but also for social identity authentication applications. (iv) The performance of real-time recognition of the proposed system is very favorable. This is because; for each class there is only one comparison to only one vector. Therefore, the system performance in terms of time is very operative for real-time applications. (v) The efficacy, effectiveness and reliability of the proposed system are evaluated on two different databases according to state of the art methods. According to the evaluation results, the proposed system is found to be very successful in terms of accuracy, time and robustness. (vi) For further research efforts, the established database used in this work is now freely available [26]. Consequently, the results of the presented system indicate that DCV-based MJP recognition offers an encouraging and robust alternative for automated identity authentication with superior accuracy and simpler and fast feature extraction method. References [1] M.A. Ferrer, C.M. Travieso, J.B. Alonso, Using hand knuckle texture for biometric identification, in: CCST’05 39th Annual 2005 International Carnahan Conference on Security Technology 2005, 11–14 October, 2005, pp. 74–78, http://dx. doi.org/10.1109/CCST.2005.1594835.
N. Ozkaya / Applied Soft Computing 37 (2015) 288–295 [2] L. Zhu, S. Zhang, Multimodal biometric identification system based on finger geometry, knuckle print and palm print, Pattern Recognit. Lett. (2010) 1641–1649. [3] K.R. Radhika, M.K. Venkatesha, G.N. Sekhar, Signature authentication based on subpattern analysis, Appl. Soft Comput. 11 (3) (2011) 3218–3228. [4] I. Iancu, N. Constantinescu, Intuitionistic fuzzy system for fingerprints authentication, Appl. Soft Comput. 13 (4) (2013) 2136–2142. [5] M. Vatsa, R. Singh, A. Noore, K. Morris, Simultaneous latent fingerprint recognition, Appl. Soft Comput. 11 (2011) 4260–4266. [6] N. Ozkaya, N. Kurat, Discriminative common vector based finger knuckle recognition, J. Vis. Commun. Image Represent. 25 (7) (2014) 1647–1675. [7] G.S. Badrinath, P. Gupta, Stockwell transform based palm-print recognition, Appl. Soft Comput. 11 (2011) 4267–4281. [8] L. Zhang, L. Zhang, D. Zhang, H. Zhu, Ensemble of local and global information for finger-knuckle-print recognition, Pattern Recognit. (2010). [9] L. Zhang, L. Zhang, D. Zhang, H. Zhu, Online finger-knuckleprint verification for personal authentication, Pattern Recognit. 43 (7) (2010) 2560–2571. [10] A. Meraoumia, S. Chitroub, A. Bouridane, Fusion of finger-knuckle-print and palmprint for an efficient multi-biometric system of person recognition, in: 2011 IEEE International Conference on Communications (ICC), 5–9 June, 2011, pp. 1–5, http://dx.doi.org/10.1109/icc.2011.5962661. [11] A. Kumar, C. Ravikanth, Personal authentication using finger knuckle surface, IEEE Trans. Inf. Forensics Secur. 4 (March (1)) (2009) 98–110, http://dx.doi.org/ 10.1109/TIFS.2008.2011089. [12] A. Kumar, Y. Zhou, Personal identification using finger knuckle orientation features, Electron. Lett. 45 (2009) 1023–1025. [13] A. Kumar, Y. Zhou, Human identification using knucklecodes, in: IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, BTAS’09, 2009, pp. 1–6. [14] L. Zhang, L. Zhang, H. Zhu, D. Zhang, L. Nan, Method and System for Identifying a Person Using Their Finger-Joint Print, United States Patent Application Publication, 2010. [15] L. Zhang, L. Zhang, D. Zhang, Finger-knuckle-print verification based on bandlimited phase-only correlation, Lect. Notes Comput. Sci. (2009) 141–148. [16] A. Morales, C.M. Travieso, M.A. Ferrer, J.B. Alonso, Improved finger-knuckleprint authentication based on orientation enhancement, Electron. Lett. (2011) 380–381. [17] C.Y. Wang, S.L. Song, F.R. Sun, L.M. Mei, A novel biometrics technology-fingerback articular skin texture recognition, Acta Automatica Sin. (2006) 360–367.
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[18] M. Chorás, R. Kozik, Knuckle biometrics based on texture features, in: Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics (ETCHB), Istanbul, Turkey, 2010, pp. 1–5. [19] L. Zhang, L. Zhang, D. Zhang, Finger-knuckle-print: a new biometric identifier, image processing (ICIP), in: 16th IEEE International Conference, 2009, pp. 1981–1984. [20] L. Zhang, L. Zhang, D. Zhang, A Novel Personal Authentication System Using Finger-knuckle-print, 2009. [21] C. Ravikanth, A. Kumar, Biometric authentication using finger-back surface, in: Proceedings of CVPR’07, 2007, pp. 1–6. [22] D.L. Woodard, P.J. Flynn, Finger surface as a biometric identifier, Comput. Vis. Image Underst. (2005) 357–384. [23] The PolyU Palmprint Database, Hong Kong Polytechnic University. Available at: http://www.Comp.polyu.edu.hk/biometrics [24] The IIT Delhi Finger Knuckle Database (Version 1.0). Available at: http://www. comp.polyu.edu.hk/∼csajaykr/knuckle/iitd knuckle.htm [25] H. Cevikalp, M. Neamtu, M. Wilkes, A. Barkana, Discriminative common vectors for face recognition, IEEE Trans. Pattern Anal. Mach. Intell. (2005) 4–13. [26] http://aves.erciyes.edu.tr/neclaozkaya/dokumanlar [27] D. Maltoni, D. Maio, A.K. Jain, S. Prabhakar, Handbook of Fingerprint Recognition, 2009. [28] http://en.wikipedia.org/wiki/Cross-validation (statistics) [29] L.F. Chen, H.Y.M. Liao, M.T. Ko, J.C. Lin, G.J. Yu, A new LDA-based face recognition system which can solve the small sample size problem, Pattern Recognit. (2000) 1713–1726. Necla Ozkaya was born in April, 1977, in Kayseri, Turkey. She received her MSc and PhD degrees in computer engineering department from Erciyes University, Turkey, in 2003 and 2009, respectively. Her research interests include biometrics, artificial neural networks and intelligent system modeling.
Contents lists available at ScienceDirect
Applied Soft Computing journal homepage: www.elsevier.com/locate/asoc
Metacarpophalangeal joint patterns based personal identification system Necla Ozkaya ∗ Erciyes University, Engineering Faculty, Computer Engineering Department, 38030 Kayseri, Turkey
a r t i c l e
i n f o
Article history: Received 10 November 2014 Received in revised form 24 January 2015 Accepted 16 May 2015 Available online 4 July 2015 Keywords: Biometrics Metacarpophalangeal joint patterns Recognition Identification Verification
a b s t r a c t This paper introduces a novel approach for identity authentication system based on metacarpophalangeal joint patterns (MJPs). A discriminative common vector (DCV) based method is utilized for feature selection. In the literature, there is no study using whole MJP for identity authentication, exceptionally a work (Ferrer et al., 2005) using the hand knuckle pattern which is some part of the MJP draws the attention as a similar study. The originality of this approach is that: whole MJP is firstly used as a biometric identifier and DCV method is firstly applied for extracting the feature set of MJP. The developed system performs some basic tasks like image acquisition, image pre-processing, feature extraction, matching, and performance evaluation. The feasibility and effectiveness of this approach is rigorously evaluated using the k-fold cross validation technique on two different databases: a publicly available database and a specially established database. The experimental results indicate that the MJPs are very distinctive biometric identifiers and can be securely used in biometric identification and verification systems, DCV method is successfully employed for obtaining the feature set of MJPs and proposed MJP based authentication approach is very successful according to state of the art techniques with a recognition rate of between 95.33% and 100.00%. © 2015 Published by Elsevier B.V.
1. Introduction Biometric technology uses certain physiological characteristics or behavioral traits to the automatic recognition of an identity. It is the most convenient and reliable alternative to satisfy the needs for personal authentication [2,3]. Hand-based biometric techniques also exploit varied internal, external and behavioral features that are unique for an individual, like fingerprints, 3D finger geometry, hand geometry, palm prints, hand veins, finger knuckle patterns, nails patterns, signatures and keystroke dynamics [1–6]. The human hand based recognition systems need very less cooperation from users for enrolment of the data whose capturing is non-intrusive. Good quality samples can be collected by low cost devices [7]. Recently, new algorithms and applications of handbased biometrics have emerged. As these are reliable, low-cost and user-friendly viable solutions to personal authentication, most of these approaches have been well investigated in the literature [8–10]. Hand-based biometric systems also have some advantages over other techniques. These systems include easy and economical data acquisition, suitability for both indoor and outdoor usage
∗ Tel.: +90 533 7335774; fax: +90 352 4375784. E-mail address: [email protected] http://dx.doi.org/10.1016/j.asoc.2015.05.056 1568-4946/© 2015 Published by Elsevier B.V.
including use in several bad weather and lighting conditions, the stability of hand features over time and the fact that they are the most user-friendly techniques in the biometrics field [8,10,11]. Although the hand-back surface may be highly useful in identity authentication due to its containing some unique patterns with rich lines, textures and creases, it has attracted very little attention from researchers [12]. The most commonly used and most popular biometric identifier from back-hand surface is finger knuckle print (FKP) [6,8–22]. Since FKP-based studies have shown significant performances as a result of the use of quick algorithms with very high accuracy, FKP recognition is a well-known technology. There are two different publicly available FKP databases for researchers who want to study FKPs [23,24]. A brief summary of FKP-based studies from the biometrics literature is given below. A study aimed at identity authentication using a 3D finger surface was introduced by Woodard and Flynn. In their study, they have developed a surface representation scheme for the middle, ring and index fingers using hand’s 3D range images. The curvature based shape index to represent the fingers’ surface was also used and a Recognition Rate (RR) of between 58% and 98% was obtained [22]. The first studies about the finger-back surface were conducted by Ravikanth and Kumar. They have used various combinations of hand geometry features, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component
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Analysis (ICA). They have obtained an Error Equal Rate (EER) of between 1.94% and 5.81% [21]. The FKP pattern was firstly used in a biometric-based recognition system and introduced as a biometric identifier by Zhang et al. The authors have developed and presented various accomplished studies about FKP recognition. A 2D Gabor filter based FKP recognition method was presented with a 97% RR and 1.09% EER value [19], a feature extraction scheme which combines orientation and magnitude information extracted by Gabor filters was introduced with a 1.51% EER value [20]. They have also used a Band-Limited Phase-Only Correlation based method with an EER of 1.68% for their experiment 1 and an EER of between 0% and 5.5% for their experiment 2 [15]. A feature extraction scheme which combines orientation and magnitude information extracted by Gabor filtering for FKP verification was introduced in the literature [9]. FKP recognition by extracting and assembling local and global FKP features was achieved with a 0.402% EER and 96.80% RR [8]. Kumar and Zhou have developed successful FKP recognition studies. They achieved personal identification using finger knuckle orientation features. A personal identification system via finger images which exploits the orientation features from the random knuckle lines using finite Radon Transform was introduced with 98.6% RR and 1.14% EER [12]. They have also used KnuckleCodes for personal authentication. The enhanced knuckle images were employed to generate KnuckleCodes using localized Radon Transform for FKP-based human identification. They have obtained a 1.08% EER [13]. Kumar and Ravikanth have achieved FKP detection and have used the geometrical features of the finger and FKP patterns for identity authentication with 1.39% EER [11]. Chorás and Kozik have used texture-based knuckle features for personal identification with 1.02% EER [18]. A method applying a Gabor filter to enhance FKP information and a Scale Invariant Feature Transform method to extract the FKP features were introduced by Morales et al. They have used fingers separately in addition to use various combinations of four fingers and obtained highly accurate results from their different experiments [16]. A multi-modal biometric system based on palm print and FKP using a Phase-Correlation Function based matching method was proposed by Meraoumia et al. [10]. DCV based method FKP recognition is also achieved by us recently [6]. Inspired by the findings on FKP, we aimed to determine if the MJP that is the biggest single pattern endowed with highly unique features and original formation on the back-hand surface found on the metacarpophalangeal bones was unique or not. We also studied with the MJP, which contains much more lines and creases than FKP, to establish if it could be used securely for personal authentication. In this study, the details of the experimental results of this investigation are presented. When the hand-based biometric literature was reviewed, no study was found on whole MJP for identity authentication. The most similar approach [1] to our work is introduced by Ferrer et al. They used the hand knuckle pattern (HKP) as a biometric identifier. HKP is some part of the MJP. Their processing procedure of the HKP is also very different from our methodology. There are time consuming pre-processing tasks like selecting the region of interest (ROI), converting the HKP image into the gray level, binarization process with a pixel by pixel thresholding, enhancement, masking, dividing the HKP image into small blocks, applying the Fast Fourier Transform and inverse of it for each blocks [1]. Our feature selection method directly employed the gray level images without any enhancement task. In this work, whole MJP is firstly used as a new biometric identifier, DCV is firstly utilized for get the feature set of the MJPs and eventually a robust biometric system with high accuracy that can be applicable for real-time usage is designed. The scheme of the proposed MJP recognition approach is introduced in Section 2. Our experiments with both the publicly available database and the established database are given in
289
Image Enrolment Image Pre-processing Feature Extraction
MJP database
Matching and Decision Making
Identification
Verification
Person’s identity
Accept or Reject
Test & Evaluation ( RR, FAR, FRR, EER, ROC )
Fig. 1. Working principles of the proposed system.
Section 3. In addition, the results acquired are also appreciated and commentated on in this section. The paper is finished by stating conclusions in Section 4. 2. MJP recognition approach In this study, an identity authentication system using MJP, found on the back of the hand, is designed and presented. Enrolment phase is the first step of the proposed approach. Pre-processing tasks are accomplished after image acquisition. In that phase fixedsize MJP images are semi-automatically obtained from the hand images and converted to gray level format. The feature selection duties are achieved afterwards with the help of DCV method. Kumar and Zhou stated in their paper [12] that because of the non-uniform reflections and a highly curved surface of the finger, FKPs have non-uniform brightness and low contrast. This makes the recognition process a complicated and intractable task. Due to the similar structures, MJPs are crucial patterns with their low contrast and non-uniform brightness. In spite of these problems, our feature selection phase extracts the features without any enhancement tasks and the matching phase also achieves the comparison using the obtained feature vectors with the help of the DCV-based method which was firstly introduced by Cevikalp et al. for face recognition [25]. The original formation of the MJP ensures to produce unique feature vectors for DCV based method. After designing the system, evaluation process should be applied. To establish a fair comparison, two different databases are used. The first one is previously used in the literature [1], and the second is a non-uniform MJP database established and used in this study. It is publicly available now for further research efforts [26]. Experimental results for both databases are very promising with RRs between 95.33% and 100%, and offering excellent potential for reliable personal identification. Fig. 1 shows the working principles of the proposed system. 2.1. Databases The performance analysis is critically important for a biometric system. To demonstrate the performance of the system a proper evaluation process should be applied with suitable databases. For evaluating the proposed system two different databases are used: the first one is the database used in [1]. It contains 20 subjects with eight images for any hand. Therefore, there are a total of
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Fig. 2. A set of hand images for a person from the first database [1].
160 images in the database. A Sony DSC-PS sensor was used to obtain the hand images. Images were captured in JPEG format and measured 2048 × 1536. The distance between the hand and a blackboard is fixed to 12 cm. Three pegs were placed on the board for positioning the hands. The proposed approach achieved 100% recognition accuracy in this uniform database. Fig. 2 shows a set of hand images for a randomly selected person from this database. To point out the superior success of the presented approach a non-uniform database was established and used in this study. For established database, non-uniform means that: it is established in different and changing conditions. Contrary to the first database, there is no specially designed environment like specific imaging system, pegs, fixed distances or settled cameras. The hand images of the established database are from 30 volunteers. 10 images are collected from each hand. Totally, the database consists of 600 hand images which are collected over a period of two weeks, in three phases, with an average interval of about seven days. A digital single-lens reflex camera with 10.2 megapixel DX format CCD is used in the image acquisition process. While taking images, the camera was not set a fixed condition, contrarily, the photos of the hands was taken manually with a variety of environmental conditions like lighting, distance between the camera and the hand, angular positions of the camera and the hand. The size of the hand images is 4608 × 2592. Since it has been created the database over three days and at varying times of the day in
natural light without extra or standard light, there is no uniform illumination in the database. The distance of the camera was randomly changed between 15 cm and 25 cm from the imaging surface. As reported in [9], these factors significantly reduce the performance of a biometric system, and to avoid these corruptive effects, researchers preferred a uniformly illuminated image acquisition device [9]. The hand image set of a randomly selected person from our established database and ROI of the last image are given in Fig. 3. The images from the established database display large variation within the same class on account of distance, angles, shadows, illuminations and pose changes. These corruptive effects make the recognition task much more stringent duty. In that case, for an ordinary method, some pre-processing tasks for MJP images like enhancement, improvement, translation, rotation are unavoidable. However, DCV-based MJP recognition approach was able to distinguish the MJP images without any of these processes; in spite of all these corruptive effects it performs the tasks with high accuracy. 2.2. Pre-processing The proposed work has two pre-processing tasks: a fixed-sized ROIs of MJPs are obtained from the hand images and these ROIs are converted to gray level format. A semi-automatic method is used for obtaining the ROIs. It works as follows: User determines the ROI of the MJP by manually clicking the center of the MJP. After
Fig. 3. A set of hand images for a person from the second database [26], and ROI of the last image.
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291
Fig. 4. ROIs of a randomly selected individual from the non-uniform database.
determination the center point of the ROI, dedicated software separates the fixed sized rectangle ROIs automatically from the hand images. ROIs of a randomly selected individual from the nonuniform database are given in Fig. 4. 2.3. Feature extraction
⊥ = span{˛k |Sw ˛k = 0, k = r + 1, . . ., d, ˛k ∈ Rd }
In Eqs. (4) and (5), d > r and r is the rank of SW . {˛1 · · · ˛d } is an orthonormal set while {˛1 · · · ˛r } is the set of orthonormal eigeni has a unique formation as given in vectors. Every MJP sample xm Eq. (6). i i i = ym + zm xm
In this study, the DCV-based feature extraction method was effectively employed to obtain the discriminative feature sets of MJPs. The DCV is an appearance-based method that was introduced and successfully applied for face recognition by Cevikalp et al. [25]. In this work, it is preferred to use the DCV method as given in that reference. Therefore, a brief explanation of the method and its working principles are borrowed it and given below; more theoretical information about the DCV method, such as the mathematical background, theorems and proofs can be found in that reference. Like other appearance-based approaches, the DCV method handles the images as two-dimensional holistic patterns. In the DCV technique, to get the common vectors within-class scatter matrix of all classes is used. The size of the null space of these matrixes is critically important for the success of the DCV technique. If this size is small, accuracy of the system is expected to be poor. In the DCVbased method, the training set contains C classes each of which are composed of N patterns. In that case, the training set is composed i represents mth sample from the ith class of M = N × C patterns. xm and it is column vector with d-dimensional. Let d > M − C. SW , SB and ST are given as follows in Eqs. (1)–(3). SW =
N C
i xm − i
i xm − i
T
(1)
C
N(i − )(i − )T
(6)
i In Eq. (6), ym
i Pxm
i QQ T xm
i V , zm
i =Q i ∈ V ⊥, Q = ¯ Q¯ T xm ¯ m = Px
= = ∈ [˛1 · · ·˛r ], Q¯ = [˛r+1 · · ·˛d ]. In addition, P is the orthogonal projection operator onto v, while P¯ is the orthogonal projection operator onto ⊥ . For any MJP, the reason for the unique decomposition is that ⊕ ⊥ = Rd . In this i = xi − yi = xi − Pxi . Especially, an case, the aim is to compute zm m m m m orthonormal basis for v obtained from the normalized eigenvectors ˛k regarding to the nonzero eigenvalues of SW . The eigenvectors may be achieved by computing the smaller M × M matrix (AT A)’s eigenvectors. SW = AAT , and A is a d × M matrix given in Eq. (7). In the method, i ) will for same class’s all patterns, the same unique vector (xcom be acquired. The optimal projection vectors are calculated after i obtaining xcom named the common vectors with Eq. (8). In Eq. (9), W is a matrix. The columns of W are the orthonormal optimal projection vectors wk . In Eq. (10), Scom is the scatter matrix of the common vectors and com is the expected value of all common vectors.
1 C A = x11 − 1 · · ·xN − 1 , x12 − 2 · · ·xN − C i xcom
=
i xm
i − QQ T xm
J(Wopt ) =
i=1 m=1
SB =
(5)
(2)
=
i Q¯ Q¯ T xm ,
(7)
m = 1, . . ., N, i = 1, . . ., C
T T arg max W SB W = arg max W ST W W T SW W =0 W T SW W =0 = arg max W T Scom W
(8)
(9)
W
i=1
ST =
N C
i xm −
T
i xm −
= SW + SB
(3)
Scom =
i xcom − com
i xcom − com
T
,
i = 1, . . ., C
(10)
i=1
i=1 m=1
In Eqs. (1)–(3), and i represent the expected value of all patterns and the expected value of the patterns in the ith class, respectively. MJP patterns were projected onto the null space of SW to obtain the optimal projection vectors W and then the projection vectors were obtained by using PCA. To achieve this task, the spanning vectors of the null space of SW must be calculated. This computation is demanding and difficult duty because of the huge dimension of SW . To accomplish this task by simplifying in a significantly lower-dimensional space, the orthogonal complement of the null space of SW is used. v and ⊥ represent the range space of SW and the null space of SW , respectively. v and ⊥ can be given as follows: = span{˛k |SW ˛k = / 0, k = 1, . . ., r, ˛k ∈ Rd }
C
(4)
Then, wk may be determined by an eigenanalysis of Scom that is the d × d matrix (Scom = Acom ATcom ). To find the eigenvectors and the nonzero eigenvalues of Scom , the smaller matrix (ATcom Acom ) sized C × C is used. Acom is the d × C matrix with the form in Eq. (11).
1 C − com · · ·xcom − com Acom = xcom
(11)
If all the common vectors are linearly independent, the C − 1 optimal projection vectors are achieved. If two common vectors are equal, then the same vector is obtained for these two classes, and the system cannot distinguish the classes. For MJP images of the i the feature vector can be achieved using the following ith class xm equation: ˝i =
i i xm , w1 · · · xm , wC−1
T
(12)
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pairs of biometric samples from the same identity [27]. Taking decision is critical step for a biometric system. In the verification mode of the biometric system, the decision is regulated by a threshold value, . Matching procedure produces a similarity measure, T. A comparison is then made T and . If two biometric features produce a T higher than or equal to , system decides that this pair of biometric images belongs to the same person. If this T is lower than , the system infers that these biometric features belong to different identities. FAR is the percentage of impostor pairs whose T is greater than or equal to , and FRR is the percentage of genuine pairs whose T is less than . There is a tradeoff between FRR and FAR that are functions of [27]. In order to make the system more tolerant with corruptive effects, is decreased, then FAR() increases. Similarly, if the system is expected to be more secure, is increased, then FRR() increases. While designing the biometric system it may not be known in advance the particular application for which the system may be used. So it is need to exhibit the system performance at all operating points () by ROC curve that is a plot of FAR against (1-FRR) [27]. ROC is one of the most commonly used evaluation metrics in the biometrics literature. EER represents the error rate at the for which FAR and FRR are equal. In biometric systems, because of the finite number of matched pair, similarity measures are not continuous. So an exact EER point might not be found, and instead of a single value an interval is used. It is commonly preferred more stringent is set to decrease FAR in spite of a rise in FRR for a biometric system [27].
3. Experimental results
Fig. 5. DCV of the MJP given in Fig. 4.
i ,w The projection coefficients of the equation xm k are independent of the sample index m. This independence guarantees 100% recognition accuracy for the train data set. The discriminative common vectors, ˝i , that were given in Eq. (13), are used for the identification process. In the test phase, for classification of the test pattern xtest , the feature vector of xtest is determined by Eq. (14). i (m = 1, . . ., N; i = 1, . . ., C) ˝i = W T xm T
˝test = W xtest
(13) (14)
After comparison with ˝i of each class, the system response will be produced. In that case, the closest discriminative common vector to ˝test is found and used to recognize the test image. The common vector obtained from the MJPs given in Fig. 4 is presented in Fig. 5. 2.4. Performance evaluation The performance of a biometric system is commonly analyzed according to some parameters like Recognition Rate (RR) indicates the accuracy of the biometric system, False Acceptance Rate (FAR) (that is also known as False Match Rate (FMR), Type I error or False Positive Rate), False Rejection Rate (FRR) (that is also called False Non-Match Rate (FNMR), Type II error or False Negative Rate), Equal-Error-Rate (EER) and The Receiver Operating Characteristic (ROC) curve. FAR and FRR are obtained from score distributions called impostor distribution that is the score distribution generated from pairs of biometric samples from different identities and genuine distribution that is the score distribution generated from
A MJP recognition system, which is explained in the previous section, is developed in this study. All the steps of the system were achieved in a proper manner and accurate results are obtained. The first step was to establish a new non-uniform hand image database [26] in addition to the publicly available database [1]. Acquiring the MJP images was from the first system duties. After completing the pre-processing tasks of the images, the MJPs were further examined to get the discriminative common vectors. The next step included the matching and decision-making stages which were performed using an Euclidean distance-based strategy. Accuracy is the most important factor used to judge a biometric-based recognition method. Therefore the test and evaluation steps were applied according to the k-fold cross validation technique on two different databases. For an identity, in the experiment with the first database, 6 MJPs were employed for training and remaining 2 MJPs for testing. In the experiment with the second database, 8 MJPs from the established non-uniform database were employed for the training and the remaining 2 for testing. In the evaluation stages, the training-test samples were selected from the databases according to the 4-fold cross validation and 5-fold cross validation technique, respectively. In cross validation, a set of data was divided into subsets called training set and testing set. In this study, the original sample data set was randomly partitioned into 4 subsamples in the first database [1], and into 5 subsamples in the second database [26]. From these subsamples, a single subsample was separated from others as the validation data for testing the system. The remaining subsamples were used as training data as explained in Ref. [28]. In the experiment with the first database, cross validation process was then repeated 4 times while 5 times in the experiment with the second database. In cross validation it is guaranteed that each of the subsamples was used exactly once as testing data. Then the results from k (k = 4 and k = 5) folds were combined by averaging to produce a single estimation. All patterns were used for both training and testing, and each pattern was used for testing exactly once by means of cross validation. In order to obtain reliable results and show the robustness of the proposed system, the training and
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c) FAR_FRR representation and ROC curve for LH of established DB [26] Fig. 6. FAR FRR representation and ROC curve of designed system for (a) publicly available DB, (b) RH of established DB and (c) LH of established DB. DB: Database, RH: Right Hand, LH: Left Hand, FAR: False Acceptance Rate, FRR: False Rejection Rate, ROC: The Receiver Operating Characteristic.
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N. Ozkaya / Applied Soft Computing 37 (2015) 288–295 Table 5 EER values of DCV-based method with small-sized databases.
Table 1 Performance comparison between classifiers [1] and present study. Classifier
EER average
HMM [1] SVM [1]
0.096 0.094
DCV [present study]
0.002
Database
Publicly available DB part1 (class size: 10) Publicly available DB part2 (class size: 10) RH of established DB part1 (class size: 15) RH of established DB part2 (class size: 15) LH of established DB part1 (class size: 15) LH of established DB part2 (class size: 15)
EER: Error Equal Rates.
Table 2 Performance evaluation of DCV-based MJP recognition scheme. Database
RR%
Publicly available DB RH of established DB LH of established DB
100.00 95.33 96.66
Table 3 EER values of DCV-based MJP recognition system.
Publicly available DB RH of established DB LH of established DB
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RR: Recognition Rate, DB: Database, RH: Right Hand, LH: Left Hand.
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Error Equal Rates (ERR%)
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89 86 89
0.0020 0.0475 0.0189
0 0.0300 0.0500
DB: Database, RH: Right Hand, LH: Left Hand, : threshold, EER: Error Equal Rates FAR: False Acceptance Rate, FRR: False Rejection Rate.
Table 4 Recognition rates of DCV-based method with small-sized databases. Database
RR%
Publicly available DB part1 (class size: 10) Publicly available DB part2 (class size: 10) RH of established DB part1 (class size: 15) RH of established DB part2 (class size: 15) LH of established DB part1 (class size: 15) LH of established DB part2 (class size: 15)
100.00 100.00 96.67 99.33 98.67 97.33
RR: Recognition Rate, DB: Database, RH: Right Hand, LH: Left Hand.
test procedure was repeated 30 times in each studied case and the RRs were calculated by averaging the errors of all the runs. A performance comparison between the proposed approach and the study given in [1] using the same database is given in Table 1. In the established database experiment, the test and evaluation were performed under different modalities: right hand (RH) and left hand (LH). Table 2 shows both the RRs of the publicly available database and the two types of hands for the established database. Table 3 shows the EER values of the DCV-based MJP recognition scheme for , for both the first database and two types of hands for the established database. It is given both FAR and FRR values corresponding to the that is the threshold value has the minimum FAR-FRR combination. The FAR and FRR representations and ROC curves of the proposed system are presented in Fig. 6(a)–(c). As is mentioned in [25,29], the size of the null space of the within-class scatter matrix is highly effective on the accuracy of the DCV based systems. Thus, the DCV-based recognition approach performs the task properly with small-sized databases. To show the real performance of the DCV-based MJP recognition approach, we also performed experiments using small-sized databases by determining the class size as 15 which was inspired from Ref. [25]. The RR and EER values of the system for small-sized databases were also obtained and are presented in Tables 4 and 5, respectively. Tables 1–5 and Fig. 6 demonstrate that the designed system achieves the tasks perfectly for both the publicly available database [1] and the established database [26]. This robust system with high
accuracy can be securely used as a biometric identification and verification system. It has much more success on small-sized databases. These results also indicate that MJPs can be reliably used to distinguish people from each other in biometric recognition systems. 4. Conclusion In this study, DCV based MJP recognition approach is introduced and a robust biometric system is designed. In the approach, whole MJP is used as a biometric identifier, DCV method is firstly used for feature selection of the MJPs. Proposed system merits to take place in the biometrics literature with the high satisfactory results a RR of 100.00% and an EER of 0.0000–0.0200 in the first database and a recognition rate of between 95.33 and 99.33% and EER of 0.0000–0.0500% in the second database. The obtained results indicate the following findings related to the proposed approach: (i) MJP based identity authentication system is designed. Whole MJP is firstly used as a biometric identifier. (ii) DCV method firstly used for feature extraction of MJPs. (iii) A sensible, robust, reliable biometric system is developed not only for high security applications but also for social identity authentication applications. (iv) The performance of real-time recognition of the proposed system is very favorable. This is because; for each class there is only one comparison to only one vector. Therefore, the system performance in terms of time is very operative for real-time applications. (v) The efficacy, effectiveness and reliability of the proposed system are evaluated on two different databases according to state of the art methods. According to the evaluation results, the proposed system is found to be very successful in terms of accuracy, time and robustness. (vi) For further research efforts, the established database used in this work is now freely available [26]. Consequently, the results of the presented system indicate that DCV-based MJP recognition offers an encouraging and robust alternative for automated identity authentication with superior accuracy and simpler and fast feature extraction method. References [1] M.A. Ferrer, C.M. Travieso, J.B. Alonso, Using hand knuckle texture for biometric identification, in: CCST’05 39th Annual 2005 International Carnahan Conference on Security Technology 2005, 11–14 October, 2005, pp. 74–78, http://dx. doi.org/10.1109/CCST.2005.1594835.
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[18] M. Chorás, R. Kozik, Knuckle biometrics based on texture features, in: Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics (ETCHB), Istanbul, Turkey, 2010, pp. 1–5. [19] L. Zhang, L. Zhang, D. Zhang, Finger-knuckle-print: a new biometric identifier, image processing (ICIP), in: 16th IEEE International Conference, 2009, pp. 1981–1984. [20] L. Zhang, L. Zhang, D. Zhang, A Novel Personal Authentication System Using Finger-knuckle-print, 2009. [21] C. Ravikanth, A. Kumar, Biometric authentication using finger-back surface, in: Proceedings of CVPR’07, 2007, pp. 1–6. [22] D.L. Woodard, P.J. Flynn, Finger surface as a biometric identifier, Comput. Vis. Image Underst. (2005) 357–384. [23] The PolyU Palmprint Database, Hong Kong Polytechnic University. Available at: http://www.Comp.polyu.edu.hk/biometrics [24] The IIT Delhi Finger Knuckle Database (Version 1.0). Available at: http://www. comp.polyu.edu.hk/∼csajaykr/knuckle/iitd knuckle.htm [25] H. Cevikalp, M. Neamtu, M. Wilkes, A. Barkana, Discriminative common vectors for face recognition, IEEE Trans. Pattern Anal. Mach. Intell. (2005) 4–13. [26] http://aves.erciyes.edu.tr/neclaozkaya/dokumanlar [27] D. Maltoni, D. Maio, A.K. Jain, S. Prabhakar, Handbook of Fingerprint Recognition, 2009. [28] http://en.wikipedia.org/wiki/Cross-validation (statistics) [29] L.F. Chen, H.Y.M. Liao, M.T. Ko, J.C. Lin, G.J. Yu, A new LDA-based face recognition system which can solve the small sample size problem, Pattern Recognit. (2000) 1713–1726. Necla Ozkaya was born in April, 1977, in Kayseri, Turkey. She received her MSc and PhD degrees in computer engineering department from Erciyes University, Turkey, in 2003 and 2009, respectively. Her research interests include biometrics, artificial neural networks and intelligent system modeling.