Resolution enhancement of images for further pattern recognition applications

Accepted Manuscript Title: Resolution Enhancement of Images for Further Pattern Recognition Applications Author: Maha Awad Fatma G. Hashad Mustafa M. Abd Elnaby Said E. El Khamy Osama S. Faragallah Alaa M. Abbas Heba A. El-Khobby El-Sayed M. El-Rabaie Salah Diab Bassiouny Sallam Saleh A. Alshebeili Fathi E. Abd El-Samie PII: DOI: Reference:

S0030-4026(15)00883-9 http://dx.doi.org/doi:10.1016/j.ijleo.2015.08.122 IJLEO 56046

To appear in: Received date: Accepted date:

12-8-2014 23-8-2015

Please cite this article as: M. Awad, F.G. Hashad, M.M.A. Elnaby, S.E.E. Khamy, O.S. Faragallah, A.M. Abbas, H.A. El-Khobby, E.-S.M. El-Rabaie, S. Diab, B. Sallam, S.A. Alshebeili, F.E.A. El-Samie, Resolution Enhancement of Images for Further Pattern Recognition Applications, Optik - International Journal for Light and Electron Optics (2015), http://dx.doi.org/10.1016/j.ijleo.2015.08.122 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Resolution Enhancement of Images for Further Pattern Recognition Applications

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Maha Awad1, Fatma G. Hashad2, Mustafa M. Abd Elnaby1, Said E. El Khamy3, Osama S. Faragallah4, Alaa M. Abbas2, Heba A. El-Khobby1, El-Sayed M. El-Rabaie2, Salah Diab, Bassiouny Sallam, Saleh A. Alshebeili, and Fathi E. Abd El-Samie2 1

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Department of Electronics and Electrical Communications, Faculty of Engineering, Tanta University, Tanta, Egypt. 2 Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, 32952, Menouf , Egypt. 3 Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, 21544, Egypt. 4 Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt Emails: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected]

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ABSTRACT

In storing large databases of images such as fingerprint and medical databases, the required memory

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size becomes a great challenge. This paper presents a framework for reducing the size of large image

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databases used in pattern recognition applications with decimation, and reconstructing the images with their

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original sizes using interpolation for feature extraction. For pattern recognition applications, a new trend based on Mel-Frequency Cepstral Coefficients (MFCCs) is presented the paper. To reconstruct the images to their original sizes, interpolation methods like bilinear, bicubic, warped-distance, and neural methods are investigated and compared. The sensitivity of the extracted features from the images to the interpolation method used is studied. For the feature extraction process, the interpolated images are converted to 1-D signals by lexicographic ordering and used in time domain or transformed to Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), or Discrete Wavelet Transform (DWT) domain. The MFCCs and polynomial shape coefficients are then extracted to generate the database of features, which can be used for pattern identification using neural networks. The pattern recognition is performed by extracting features from the pattern image under test with the same manner used in the training step. Experimental results show that feature extraction from an interpolated image to retain the original image dimensions can be used 1

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robustly for pattern recognition. In addition, results also show that the DCT is the most appropriate domain for feature extraction.

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Keywords: Image interpolation, Pattern recognition, Cepstral analysis.

INTRODUCTION

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Pattern recognition applications depend mainly on large stored databases that are used for feature extraction and training of a classifier for possible recognition on new incoming data samples. The main

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problem encountered in these applications is the required tremendous storage size. This problem has

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motivated the search to find storage reduction solutions that have no or small effect on the pattern recognition process. In this paper, we try to find a solution for this problem. We use decimation as a tool to

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reduce the size of the data, and then image interpolation to reconstruct the image with original size before

investigated in the paper.

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feature extraction. The effect on this decimation-interpolation process on the pattern recognition is

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Image interpolation is the process by which a high resolution image is obtained from a low resolution one. It allows the user to increase the size of images interactively, to concentrate on some details or to get a better overview of a certain part of the image. Image interpolation has a wide range of applications in numerous fields such as medical image processing, military applications, space imagery and remote sensing. The image interpolation problem has been intensively treated in the literature [1-5]. The optimal approach for image interpolation is based on the popular sinc function as the best interpolating basis function. However, the sinc function decays too slowly at infinity, and it is difficult to realize this function physically. Hence, different approximations such as the bilinear, bicubic and cubic Spline approximations have been proposed [3,4,5,6] to solve this problem. These conventional techniques are space-invariant algorithms based on certain basis functions. They do not take into consideration the spatial activity of the image to be interpolated. 2

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Recently, a linear space-invariant approach was proposed for image interpolation [7]. This approach is based on the evaluation of a “warped distance” between the pixel to be estimated and each of its neighbors. The warping process is performed by moving the estimation of the pixel towards the more homogeneous neighboring side. This algorithm has led to better results, especially for edge interpolation. Neural networks

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have been successfully applied in the area of signal and image processing, including image interpolation. For instance, Plaziac [8] proposed a neural-based image interpolation method in noise-free and noisy line

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doubling and image expansion problems. He reported promising results, particularly under noisy conditions.

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In this paper, we investigate several image interpolation methods for the pattern recognition applications. The rest of the paper is organized as follows. Section II describes the image interpolation methods used

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in the paper. Section III introduces the some characteristics of the pattern images used in recognition applications. Section IV demonstrates the proposed framework for image storage and pattern recognition. In

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Section V, the experimental results are given. Finally, Section VI summarizes the concluding remarks.

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IMAGE INTERPOLATION METHODS

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II.

A. LINEAR SPACE-INVARIANT IMAGE INTERPOLATION

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The process of image interpolation aims at estimating intermediate pixels between the known pixel values. This process is performed on a 1-D basis, row-by-row, and then column by column. If we have a discrete sequence f(xk) of length N as shown in Fig. (1-a), and this sequence is filtered and down-sampled by 2, we get another sequence g(xn) of length N/2 as shown in Fig. (1-b). The interpolation process aims at estimating a sequence l(xk) of length N as shown in Fig.(1-c), which is as close as possible to the original discrete sequence f(xk).

For equally-spaced 1-D sampled data, g ( x n ) , many interpolation functions can be used. The value of the sample to be estimated, l ( x k 1 ) , can, in general, be written in the form [1]:

l ( xk 1 ) 



 c  (x

n  

n

k 1

 xn )

(1)

where  ( x ) is the interpolation basis function. 3

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From the classical sampling theory [3, 7], if g (xn) is band-limited to [-,], then: l ( x k 1 ) 



 g(x

n  

n

)sinc( x k 1  x n )

(2)

This is known as ideal interpolation. From the numerical computations perspective, the ideal interpolation

such as the bilinear, bicubic and cubic Spline are used as alternatives [3, 7].

s  x k 1  x n

1 - s  x n 1  x k 1

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,

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As shown in Fig .1, we define the distance of xk+1 from xn and xn+1 as [6, 7]:

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formula is not practical due to the slow rate of decay of the interpolation kernel sinc(x). So, approximations

For the bilinear, bicubic and cubic Spline image interpolation, we have [3,4]:

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i- Bilinear l ( x k 1 )  (1  s) g ( x n )  sg ( x n 1 )

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ii- Bicubic

l ( x k  1 )  g ( x n  1 )(  s 3  2 s 2  s ) / 2  g ( x n )( 3 s 3  5 s 2  2 ) / 2

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iii- Cubic Spline

(4)

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 g ( x n  1 )(  s 3  4 s 2  s ) / 2  g ( x n  2 )( s 3  s 2 ) / 2

(3)

l( xk 1 )  g( xn1 )[(3  s) 3  4(2  s) 3  6(1 s) 3  4s 3 ] / 6

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 g( xn )[(2  s) 3  4(1 s) 3  6s 3 ] / 6  g( xn1 )[(1 s) 3  4s 3 ] / 6

(5)

 g( xn2 )s / 6 3

4

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For cubic Spline interpolation, the a pre-filtering step is used to calculate the Spline coefficients, and then Eq. (5) is implemented [3, 7].

f(xk-1)

f(xk)

f(xk+1)

f(xk+2)

1/2

bg(xn)

1/2

1/2

g(xn+1)

g(xn)

1

l(xk)

l(xk-1)

l(xk+1)





s 1/2

dc(xn)

c(xn)

1/2

1/2

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1/2

l(xk+2)

1-s

c(xn+1)

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cl(xk)

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1/2

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af(xk)

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Fig. (1) Signal down-sampling and interpolation. Original data sequence.

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b- Down-sampled version of the original data sequence. c- Interpolated data sequence.

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d- Down-sampled version of the interpolated data sequence.

For 2-D image interpolation, all of these techniques are applied along rows and then along columns. B. WARPED-DISTANCE IMAGE INTERPOLATION The idea of the warped distance can be used in any of the three above-mentioned methods, to improve their performance. This idea is based on modifying the distance s and using a new distance s’ based on the homogeneity in the neighborhood of each estimated pixel. The warped distance s’ can be estimated using the following relation [6]: s'  s   An s ( s  1)

(6)

where An refers to the asymmetry of the data in the neighborhood of x, and it is defined as [7]: An 

g ( xn 1 )  g ( x n1 )  g ( xn  2 )  g ( x n )

(7)

L 1

5

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where L=256 for 8 bit pixels. The scaling factor L is to keep An in the range of –1 to 1. The parameter  controls the intensity of warping. It has a positive value and may be equal to 1 or 2. The desired effect of this warping is to avoid blurring of the edges in the interpolation process.

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C. NEURAL IMAGE INTERPOLATION In order to implement the interpolator using a neural network, it should first be trained. We can use a

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feed-forward neural network as shown in Fig. 2. A typical feed-forward neural network has an input layer, a number of hidden layers, and an output layer. Training a neural network is accomplished by adjusting its

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weights using a training algorithm. The training algorithm adapts the weights by attempting to minimize the

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sum of the squared error between a desired output and the actual output of the output neurons given by [9, 10]: 1 O  Do  Yo 2 2 o 1

(8)

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E

where Do and Yo are the desired and actual outputs of the oth output neuron, respectively, and O is the

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number of output neurons. Each weight in the neural network is adjusted by adding an increment to reduce E

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as rapidly as possible. The adjustment is carried out over several training iterations, until a satisfactorily

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small value of E is obtained or a given number of epochs is reached. The error back-propagation algorithm can be used for this task [9, 10].

Fig. (2) A feed-forward neural network.

The steps of the proposed neural implementation of polynomial interpolation can be summarized as follows: 6

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In the training phase: 1-

A set of images is interpolated using a certain polynomial interpolation technique.

2-

The input points of the region of support used for each pixel estimation are sorted in a

vector form (2 points for bilinear and four points for bicubic and cubic Spline interpolation). All vectors are used as inputs to the neural network with the interpolation result as

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3-

outputs for training.

In the image to be interpolated, the input points of the region of support used for each

pixel estimation are sorted in a vector form.

This vector is used as an input to the neural network to estimate the required pixel

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2-

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1-

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In the testing phase:

value.

III.

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A neural network of any fixed size can be used with all types of interpolation. Pattern Characteristics and Recognition

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The concept of image pattern recognition is based on storing a large amount of images in a database

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to compare them to any new sample to decide whether it belongs to this database or not. The main problem

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with databases is the huge storage size. We can solve this problem by storing down-sampled versions of the original images in the database. The original image could be retrieved, when needed, by image interpolation. The proposed idea of saving down-sampled images rather than the original ones can be used with different types of databases such as fingerprint and landmine. We will try each of them. We will also present a robust feature extraction method suitable for the proposed application. This method is based on cepstral features. Fingerprints are biometric signs that can be utilized for identification and authentication purposes in biometric systems. Among all the biometric indicators, fingerprints have one of the highest levels of reliability [12].

The main reasons for the popularity of the fingerprint-based identification are the

uniqueness and permanence of fingerprints. It has been claimed that no two individuals, including identical twins, have the exact same fingerprints. It has also been claimed that the fingerprint of an individual does not change throughout his lifetime, with the exception of a significant injury to the finger that creates a permanent scar [13]. 7

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Fingerprints are graphical patterns of locally parallel ridges and valleys with well-defined orientations on the surface of fingertips. Ridges are the lines on the tip of one's finger. The unique pattern of lines can either be loop, whorl, or arch pattern. Valleys are the spaces or gaps that are on either side of a ridge. The most important features in fingerprints are called the minutiae, which are usually defined as the ridge endings and

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the ridge bifurcations. A ridge bifurcation is the point, where a ridge forks into a branch ridge [14]. Examples of minutiae are shown in Fig. (3). A full fingerprint normally contains between 50 to 80 minutiae.

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A partial fingerprint may contain fewer than 20 minutiae. According to the Federal Bureau of Investigations,

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it suffices to identify a fingerprint by matching 12 minutiae, but it has been reported that in most cases, 8

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matched minutiae are enough.

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Fig. (3) Examples of minutiae points. Landmines are small explosive objects that are buried under the earth's surface. They are classified

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as Anti-Personnel (AP) landmines, which are used to kill persons, and Anti-Tank (AT) landmines, which are used to attack vehicles and their occupants. There are about 100 million buried landmines covering more than 200,000 square kilometers of the world surface and affecting about 70 countries [15, 16]. Many obstacles are faced in removing these buried landmines, such as the loss or absence of maps or information about these mines or even the areas, where they were laid in, the change of mine locations due to climatic and physical factors, the large variety of types of AP and AT landmines, and the high costs needed to remove mines. It is known that the production cost of landmines is very low (may be $3 per mine), but the detection and removal cost is high (more than $1000 per mine). Several techniques have been proposed for demining (detecting and clearing) these buried mines. One of the promising landmine detection techniques is the acoustic to seismic A/S technique, which performs the detection of landmines by vibrating them with acoustic or seismic waves that are generated and 8

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received by acoustic or seismic transducers, respectively [17,18]. An acoustic or a seismic wave is excited by a source at a known position. It travels through the soil to interact with underground objects. This technique consists of a transmission system, which generates the acoustic or the seismic wave into the area

mechanical changes are used to produce acoustic images for the areas under test.

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under test and a receiving system, which senses the changes in the mechanical properties of this area. These

Detection of landmines from acoustic images can be accomplished by traditional shape-based

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techniques. These techniques begin by intensity thresholding of images to reject the dark background and

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then detection of landmines based on their dimensions [15, 16]. The drawbacks of these techniques lie in their inability to detect landmines with small dimensions and their inability to reject background noise in the

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intensity thresholding approach. In most of these techniques, pre-processing steps like morphological operations are required. In spite of the ability of morphological operations to smooth objects in images, they

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may close small clutter shapes to give false alarms.

The detection of a landmine can be achieved by the intensity thresholding of the landmine image with a

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certain threshold to remove the dark background. This process helps eliminating the background and leaves

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objects, only. Then an area thresholding process is performed based on the areas of the expected objects to A pre-processing step, such as the use of morphological

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remove the unwanted small-area clutters.

operations, may be required prior to thresholding. Fig (4) shows a block diagram of a traditional landmine detection technique.

Traditional landmine detection based on geometrical information has several limitations. The intensity thresholding may not remove all unwanted noise or clutter in images. The area thresholding process requires certain thresholds which may differ from image to image leading to either false alarms or missed landmines. Without preprocessing, the detection probability of landmines is about 90%. Morphological preprocessing can increase this detection probability to about 97% with a small false alarm probability, but all these probabilities are in the absence of noise [15, 16]. The issue of noise effect is an issue, which is rarely studied by researchers in this area. A new cepstral approach valid for fingerprint and landmine pattern recognition is presented in this paper. To reduce the size of large databases, decimated or down-sampled images are saved, and the different 9

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interpolation techniques are investigated to obtain the images with the original size. The sensitivity of the proposed cepstral feature extraction method for the synthetic pixels obtained through interpolation is studied. Histogram first trough estimation

Histogram estimation

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Landmine image

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Intensity Thresholding

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Preprocessing

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Area Thresholding

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Decision Making

Landmine or no landmine

Fig. (4) Steps of the traditional landmine detection.

IV.

Proposed Pattern Recognition Method

The proposed pattern recognition method has two phases; a training phase and a testing phase. In the training phase, features are extracted from the training images. These features are used to train a neural network. In the testing phase, features are extracted from every incoming image and a feature matching process is performed to decide whether these features belong to a previously known pattern or not. The block diagram of the proposed recognition system is shown in Fig. (5). The steps of the feature extraction process from a fingerprint image can be summarized as follows: 10

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1- The image is lexicographically ordered into a 1-D signal. 2- The obtained 1-D signal can be used in time domain or in another discrete transform domain. The DCT, DST and DWT can be used for this purpose. 3- MFCCs and polynomial shape coefficients are extracted from either the 1-D signal, the discrete

Training of a neural network

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To database

Feature extraction (MFCCs + Polynomial coefficients)

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Discrete transform (DCT, DST or DWT)

Lexicographic ordering

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Training image

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transform of the signal, or both of them.

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(a) Training Phase

Discrete transform (DCT, DST or DWT)

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Lexicographic ordering

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Test image

Identified pattern or not

Decision making

Feature extraction (MFCCs+ Polynomial coefficients)

Feature matching with the trained neural network

(b) Testing phase.

Fig. (5) Block diagram of the proposed pattern recognition method.

The concept of feature extraction using the MFCCs is widely known in speaker identification [1928]. It contributes to the goal of identifying speakers based on the low-level properties. Pattern images after lexicographic ordering are treated in this paper like speech signals. In speaker identification, the extraction provides sufficient information for good speaker discrimination. Experimental results will show a great success if the ideas of feature extraction from speech signals are applied to 1-D pattern signals.

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The MFCCs are commonly extracted from signals through cepstral analysis. The input signal is first framed and windowed, the Fourier transform is then taken, and the magnitude of the resulting spectrum is warped on the Mel-scale. The log of this spectrum is then taken and the DCT is applied [19, 20]. Fig (6)

Windowing

DFT

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Mel Cepstrum

Lexicographic ordering

IDFT

Log

Mel-frequency warping

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Pattern image

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shows the steps of extraction of MFCCs from an image.

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Fig. (6) Cepstral transformation of a 1-D pattern signal.

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The 1-D signal must first be broken up into small segments; each of N samples. These segments are called frames and the motivation for this framing process is the quasi-stationary nature of the 1-D signals.

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However, if we examine the signal over discrete segments, which are sufficiently short in duration, then

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these segments can be considered as stationary and exhibit stable characteristics [19, 20]. To avoid loss of

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information, frame overlap is used. Each frame begins at some offset of L samples with respect to the previous frame, where L≤N.

The MFCCs are sensitive to mismatches or time shifts between training and testing data. As a result, there is a need for other coefficients to be added to the MFCCs to reduce this sensitivity. Polynomial coefficients can be used for this purpose. These coefficients can help in increasing the similarity between the training and the testing signals. If each MFCC is modeled as a time waveform over adjacent frames, polynomial coefficients can be used to model the slope and curvature of this time waveform. Adding these polynomial coefficients to the MFCCs vector will be helpful in reducing the sensitivity to any mismatches between the training and testing data [25-28]. The classification step in the proposed recognition method is in fact a feature matching process between the features of a new image and the features saved in the database. Neural Networks are widely used for 12

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feature matching. Multi-Layer Perceptrons (MLPs) consisting of an input layer, one or more hidden layers, and an output layer can be used for this purpose [29, 30]. V.

Experimental Results

Several experiments have been carried out to test the performance of the proposed cepstral pattern

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recognition method after several types of image interpolation to retain the original image. Spatial and transform domains are used for feature extraction after interpolation. The degradations considered are

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AWGN, impulsive noise, and speckle noise with and without blurring. In the training phase of the proposed

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recognition method, a database is first composed. Twenty images are used to generate the database. The MFCCs and polynomial coefficients are estimated to form the feature vectors of the database. In the testing

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phase, similar features to those used in the training are extracted from the degraded fingerprint images after interpolation and used for matching.

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Seven methods are used for feature extraction. In the first method, the MFCCs and the polynomial coefficients are extracted from the spatial domain signals, only. In the second method, the features are

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extracted from the DWT of these signals. In the third method, the features are extracted from both the

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original signals and the DWT of these signals and concatenated. In the fourth method, the features are

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extracted from the DCT of the spatial domain signals. In the fifth method, the features are extracted from both the original signals and the DCT of these signals and concatenated. In the sixth method, the features are extracted from the DST of the spatial-domain signals. In the last method, the features are extracted from both the original signals and the DST of these signals and concatenated. Samples of the fingerprint and landmine images used in the database are shown in Fig. (7). The results of the experiments on fingerprint images are given in Figs. (8) to (14). Also, the results of experiments on interpolated landmine images are given in Fig. (15) to (21). From these figures, it is clear that feature extraction from transform domains like the DCT and DST are not sensitive to synthetic pixels obtained through all types of interpolation. This is attributed to the averaging effect of the transformation equation which cancels the effect of pixel synthesis errors. The results also reveal that neural interpolation is feasible with the proposed pattern recognition method.

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(a) Fingerprint images.

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(b) Landmine images.

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Fig. (7) Samples of the images used in the training phase.

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Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

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from from from from from from from

signal the DWT of the signal the signal plus the DWT of the signal DCT of signal signal plus DCT of signal DST of Signal signal plus DST of signal

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Fig. (8) Recognition rate variation with degradation for the different feature extraction methods from degraded fingerprint images.

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80

Ac ce p

(c) Blurring with AWGN.

100

80

et a R n oti i n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

60

40

20

0 0

1

2

3

4 5 6 Error Percentage

7

8

9

10

(d) Blurring with impulsive noise.

17

Page 17 of 48

100

80

60

40

ip t

et a R n oi ti n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

0.01

0.02

0.03

0.04 0.05 0.06 Noise Variance

(e) Speckle noise.

80

0.1

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

M

60

40

0.02

0.03

Ac ce p

0.01

te

20

0 0

0.09

d

et a R n oti i n g o c e R

0.08

an

100

0.07

us

0 0

cr

20

0.04 0.05 0.06 Noise Variance

0.07

0.08

0.09

0.1

Fig. (f) Blurring with speckle noise.

Fig. (9) Recognition rate variation with degradation for the different feature extraction methods from degraded fingerprint images with bilinear interpolation.

100

Features Features Features Features Features Features Features

80

et a R n oi ti n g o c e R

60

40

from from from from from from from

signal the DWT of the signal the signal plus the DWT of the signal DCT of signal signal plus DCT of signal DST of Signal signal plus DST of signal

20

0 0

5

10

15

20

25 30 SNR (dB)

35

40

45

50

(a) AWGN.

18

Page 18 of 48

100

80

60

40

ip t

et a R n oi ti n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

1

2

3

4 5 6 Error Percentage

(b) Impulsive noise.

80

10

M

60

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

40

10

15

Ac ce p

5

te

20

0 0

9

d

et a R n oti i n g o c e R

8

an

100

7

us

0 0

cr

20

20

25 30 SNR (dB)

35

40

45

50

(c) Blurring with AWGN.

100

80

et a R n oti i n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

60

40

20

0 0

1

2

3

4 5 6 Error Percentage

7

8

9

10

(d) Blurring with impulsive noise.

19

Page 19 of 48

100

80

60

40

ip t

et a R n oi ti n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

0.01

0.02

0.03

0.04 0.05 0.06 Noise Variance

100

80

0.1

M

60

d

40

20

0.01

0.02

0.03

Ac ce p

0 0

0.09

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

te

et a R n oti i n g o c e R

0.08

an

(e) Speckle noise.

0.07

us

0 0

cr

20

0.04 0.05 0.06 Noise Variance

0.07

0.08

0.09

0.1

(f) Blurring with speckle noise.

Fig. (10) Recognition rate variation with degradation for the different feature extraction methods from degraded fingerprint images with bicubic interpolation.

20

Page 20 of 48

100

80

60

40

ip t

et a R n oi ti n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

5

10

15

20

25 30 SNR (dB)

( a) AWGN.

80

50

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

M

60

40

2

Ac ce p

1

te

20

0 0

45

d

et a R n oti i n g o c e R

40

an

100

35

us

0 0

cr

20

3

4 5 6 Error Percentage

7

8

9

10

(b) Impulsive noise.

100

80

et a R n oti i n g o c e R

60

40

20

0 0

5

10

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal 15

20

25 30 SNR (dB)

35

40

45

50

(c) Blurring with AWGN.

21

Page 21 of 48

100

80

60

40

ip t

et a R n oi ti n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

0 0

1

2

3

4 5 6 Error Percentage

7

8

80

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

M

60

d

40

0.02

0.03

Ac ce p

0.01

te

20

0 0

10

an

100

et a R n oti i n g o c e R

9

us

(d) Blurring with impulsive noise.

cr

20

0.04 0.05 0.06 Noise Variance

0.07

0.08

0.09

0.1

(e) Speckle noise.

100

80

et a R n oti i n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

60

40

20

0 0

0.01

0.02

0.03

0.04 0.05 0.06 Noise Variance

0.07

0.08

0.09

0.1

(f) Blurring with speckle noise. Fig. (11) Recognition rate variation with degradation for the different feature extraction methods from degraded fingerprint images with warped-distance bilinear interpolation.

22

Page 22 of 48

100

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

80

60

40

ip t

et a R n oi ti n g o c e R

5

10

15

20

25 30 SNR (dB)

(a) AWGN.

80

50

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

M

60

40

2

Ac ce p

1

te

20

0 0

45

d

et a R n oti i n g o c e R

40

an

100

35

us

0 0

cr

20

3

4 5 6 Error Percentage

7

8

9

10

(b) Impulsive noise.

100

80

et a R n oti i n g o c e R

60

40

20

0 0

5

10

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal 15

20

25 30 SNR (dB)

35

40

45

50

(c) Blurring with AWGN.

23

Page 23 of 48

100

80

60

40

ip t

et a R n oi ti n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

0 0

1

2

3

4 5 6 Error Percentage

7

8

80

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

M

60

d

40

0.02

0.03

Ac ce p

0.01

te

20

0 0

10

an

100

et a R n oti i n g o c e R

9

us

(d) Blurring with impulsive noise.

cr

20

0.04 0.05 0.06 Noise Variance

0.07

0.08

0.09

0.1

(e) Speckle noise.

100

80

et a R n oi ti n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

60

40

20

0 0

0.01

0.02

0.03

0.04 0.05 0.06 Noise Variance

0.07

0.08

0.09

0.1

(f) Blurring with speckle noise. Fig. (12) Recognition rate variation with degradation for the different feature extraction methods from degraded fingerprint images with warped-distance bicubic interpolation.

24

Page 24 of 48

100

80

ip t

60

40

20

5

10

15

20

25 30 SNR (dB)

100

80

50

M

60

d

40

20

1

2

Ac ce p

0 0

45

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

te

et a R n oti i n g o c e R

40

an

(a) AWGN.

35

us

0 0

cr

et a R n oti i n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

3

4 5 6 Error Percentage

7

8

9

10

(b) Impulsive noise.

100

80

et a R n oti i n g o c e R

60

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

40

20

0 0

5

10

15

20

25 30 SNR (dB)

35

40

45

50

(c) Blurring with AWGN.

25

Page 25 of 48

100

80

60

40

ip t

et a R n oi ti n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

0 0

1

2

3

4 5 6 Error Percentage

7

8

80

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

M

60

d

40

0.02

0.03

Ac ce p

0.01

te

20

0 0

10

an

100

et a R n oti i n g o c e R

9

us

(d) Blurring with impulsive noise.

cr

20

0.04 0.05 0.06 Noise Variance

0.07

0.08

0.09

0.1

(e) Speckle noise.

100

80

et a R n oti i n g o c e R

60

40

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

20

0 0

0.01

0.02

0.03

0.04 0.05 0.06 Noise Variance

0.07

0.08

0.09

0.1

(f) Blurring with speckle noise.

26

Page 26 of 48

Fig. (13) Recognition rate variation with degradation for the different feature extraction methods from degraded fingerprint images with neural implementation of bilinear interpolation. 100

80

ip t

60

40

cr

et a R n oti i n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

0 0

5

10

15

20

25 30 SNR (dB)

80

45

50

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

d

60

te

40

Ac ce p

20

0 0

40

M

100

et a R n oti i n g o c e R

35

an

(a) AWGN.

us

20

1

2

3

4 5 6 Error Percentage

7

8

9

10

(b) Impulsive noise.

100

80

et a R n oti i n g o c e R

60 Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

40

20

0 0

5

10

15

20

25 30 SNR (dB)

27

35

40

45

50

Page 27 of 48

(c) Blurring with AWGN. 100

80

ip t

60

40

20

1

2

3

4 5 6 Error Percentage

7

8

9

10

us

0 0

cr

et a R n oti i n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

an

(d) Blurring with impulsive noise. 100

80

M

60

d

40

20

0.01

0.02

0.03

Ac ce p

0 0

te

et a R n oti i n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

0.04 0.05 0.06 Noise Variance

0.07

0.08

0.09

0.1

(e) Speckle noise.

100

80

et a R n oti i n g o c e R

60

40

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

20

0 0

0.01

0.02

0.03

0.04 0.05 0.06 Noise Variance

0.07

0.08

0.09

0.1

(f) Blurring with speckle noise.

28

Page 28 of 48

Fig. (14) Recognition rate variation with degradation for the different feature extraction methods from degraded fingerprint images with neural implementation of bicubic interpolation.

100

cr

60

40

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

20

5

10

15

20

25 30 SNR (dB)

35

40

45

50

9

10

an

0 0

us

et a R n oti i n g o c e R

ip t

80

(a) AWGN.

M

100

te

60

40

Ac ce p

et a R n oti i n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

d

80

20

0 0

1

2

3

4 5 6 Error Percentage

7

8

(b) Impulsive noise.

100

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

80 et a R n oti i n g o c e R

60

40

20

0 0

5

10

15

20

25 30 SNR (dB)

29

35

40

45

50

Page 29 of 48

(c) Blurring with AWGN. 100

60

40

cr

et a R n oti i n g o c e R

ip t

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

80

0 0

1

2

3

4 5 6 Error Percentage

us

20

7

8

9

10

an

(d) Blurring with impulsive noise.

M

100

80

40

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

Ac ce p

20

d

60

te

et a R n oti i n g o c e R

0 0

0.01

0.02

0.03

0.04 0.05 0.06 Noise Variance

0.07

0.08

0.09

0.1

0.07

0.08

0.09

0.1

(e) Speckle noise.

100

80

et a R n oti i n g o c e R

60

40

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

20

0 0

0.01

0.02

0.03

0.04 0.05 0.06 Noise Variance

30

Page 30 of 48

(f) Blurring with speckle noise.

Fig. (15) Recognition rate variation with degradation for the different feature extraction methods from degraded landmine images.

ip t

100 Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

60

40

us

et a R n oti i n g o c e R

cr

80

0 0

5

10

15

20

an

20

25 30 SNR (dB)

35

40

45

50

M

(a) AWGN.

te

80

60

40

Ac ce p

et a R n oti i n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

d

100

20

0 0

1

2

3

4 5 6 Error Percentage

7

8

9

10

(b) Impulsive noise.

31

Page 31 of 48

100 Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

80

60

40

ip t

et a R n oi ti n g o c e R

0 0

5

10

15

20

25 30 SNR (dB)

35

40

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

80

M

60

d

40

2

3

Ac ce p

1

te

20

0 0

50

an

100

et a R n oti i n g o c e R

45

us

(c) Blurring with AWGN.

cr

20

4 5 6 Error Percentage

7

8

9

10

0.07

0.08

0.09

0.1

(d) Impulsive noise.

100

80

et a R n oti i n g o c e R

60

40

20

0 0

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

0.01

0.02

0.03

0.04 0.05 0.06 Noise Variance

(e) Speckle noise.

32

Page 32 of 48

100

et a R n oi ti n g o c e R

60

40

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

ip t

80

0 0

0.01

0.02

0.03

0.04 0.05 0.06 Noise Variance

0.07

cr

20

0.08

0.1

us

(f) Blurring with speckle noise.

0.09

Fig. (16) Recognition rate variation with degradation for the different feature extraction methods from degraded landmine images

an

with bilinear interpolation.

M

100

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

te

60

40

Ac ce p

et a R n oti i n g o c e R

d

80

20

0 0

5

10

15

20

25 30 SNR (dB)

35

40

45

50

9

10

(a) AWGN.

100

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

80 et a R n oti i n g o c e R

60

40

20

0 0

1

2

3

4 5 6 Error Percentage

33

7

8

Page 33 of 48

(b) Impulsive noise.

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

80

60

40

cr

et a R n oti i n g o c e R

5

10

15

20

25 30 SNR (dB)

us

20

0 0

ip t

100

35

40

45

50

9

10

an

(c) Blurring with AWGN.

M

100

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

80

d

60

40

te

et a R n oti i n g o c e R

Ac ce p

20

0 0

1

2

3

4 5 6 Error Percentage

7

8

(d) Blurring with impulsive noise.

34

Page 34 of 48

100

80

60

0 0

0.01

0.02

0.03

ip t

20

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

0.04 0.05 0.06 Noise Variance

(e) Speckle noise.

60

40

0.02

0.03

Ac ce p

0.01

te

20

0 0

0.1

d

et a R n oti i n g o c e R

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

0.09

M

80

0.08

an

100

0.07

cr

40

us

et a R n oi ti n g o c e R

0.04 0.05 0.06 Noise Variance

0.07

0.08

0.09

0.1

(f) Blurring with speckle noise.

Fig. (17) Recognition rate variation with degradation for the different feature extraction methods from degraded landmine images with bicubic interpolation.

35

Page 35 of 48

100

80

60

40

20

0 0

5

10

15

20

25 30 SNR (dB)

( a) AWGN.

50

M

60

d

40

2

Ac ce p

1

te

20

0 0

45

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

80 et a R n oti i n g o c e R

40

an

100

35

cr

ip t

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

us

et a R n oi ti n g o c e R

3

4 5 6 Error Percentage

7

8

9

10

(b) Impulsive noise.

100

80

et a R n oti i n g o c e R

60

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

40

20

0 0

5

10

15

20

25 30 SNR (dB)

35

40

45

50

(c) Blurring with AWGN.

36

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100 Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

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(d) Blurring with impulsive noise.

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Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

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et a R n oti i n g o c e R

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Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

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0 0

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(f) Blurring with speckle noise. Fig. (18) Recognition rate variation with degradation for the different feature extraction methods from degraded landmine images with warped-distance bilinear interpolation.

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100

Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

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Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

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100 Features from signal Features from the DWT of the signal Features from the signal plus the DWT of the signal Features from DCT of signal Features from signal plus DCT of signal Features from DST of Signal Features from signal plus DST of signal

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(e) Speckle noise.

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Fig. (20) Recognition rate variation with degradation for the different feature extraction methods from degraded landmine images with neural implementation of bilinear interpolation.

100

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(a) AWGN. 100

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0.04 0.05 0.06 Noise Variance

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(f) Blurring with speckle noise.

Fig. (21) Recognition rate variation with degradation for the different feature extraction methods from degraded landmine images with neural implementation of bicubic interpolation.

VI.

Conclusions

This paper presented a new cepstral method for feature extraction for pattern recognition applications. In the proposed method, images are transformed to 1-D signals and the MFCCs and polynomial coefficients are extracted from them. Features are extracted from the 1-D signals and/or their transforms. A database of the cepstral features of pattern images is generated in the training phase and used for feature matching in the testing phase after interpolation with different types of interpolation methods. The proposed method is mostly used in speaker identification, but experimental results show that it can also be used for feature 44

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extraction from images. Feature extraction from the different transform domains have been tested, and it was noticed that features extracted from the DCTs of the 1-D pattern signals are the most powerful among all other features. This is attributed to the energy compaction property of the DCT, which makes the features extracted from the first samples after the DCT powerful enough to characterize the signals. Results have also

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shown that recognition rates up to 100 % for pattern are possible in the absence of degradations.

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Computer Engineering & Systems, Cairo, Egypt, November 2008.

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on Acoustics, Speech, And Signal Processing, Vol. ASSP-29, No. 2, pp. 254-272, 1981. R. Gandhiraj, P.S. Sathidevi, " Auditory-based Wavelet Packet Filter-bank for Speech

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For Salah Diab and Bassiouny Sallam Facullty of Electronic Engineering , Menoufia University, Menouf, Egypt.

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For Saleh A. Alshebeili 2KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), King Saud University.

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