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A Novel Approach to Brain Biometric User Recognition
Available online at www.sciencedirect.com
ScienceDirect Procedia Technology 25 (2016) 240 – 247
Global Colloquium in Recent Advancement and Effectual Researches in Engineering, Science and Technology (RAEREST 2016)
A Novel Approach to Brain Biometric User Recognition Reshmi K.C.1, Ihsana Muhammed P., Priya V.V., Akhila V.A. 1
Dept of Computer Science &Engineering, Royal College of Engineering & Technology, Chiramanangad P.O., Thrissur, 680604, India [email protected], [email protected],[email protected],[email protected]
Abstract Advances in the field of information technology makes information security is a major part of it. To deal with security, Recognition plays an important role .Now a day the numbers of organizations are using automated person recognition systems to improve customer satisfaction, to enhance operating efficiency and to secure critical resources. The reliable biometric recognition systems to either confirm or determine the individual’s identity to servicing their requests. This paper gives a novel approach on user recognition using EEG signals of Brain. EEG is sensitive to the electrical field generated by the electric currents induced in the brain. EEG recordings are acquired with portable and relatively inexpensive devices when compared to the other brain imaging technique like fMRI. The Emotiv EPOC EEG neuro headset has 14 saline electrodes with two reference sensors is used for the acquisition of brainwaves. © 2016 2015The TheAuthors. Authors.Published Published Elsevier Ltd. © byby Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of RAEREST 2016. Peer-review under responsibility of the organizing committee of RAEREST 2016
Keywords: Brain waves; BCI; EEG. 1. Introduction The term Biometrics [1] originated from two Greek words bios=”life” and metron = “measure”. Biometrics deals with the automated recognition of individuals based on their behavioural and physical characteristics. Mainly two types of Biometrics are there. Conventional Biometrics [2] and Cognitive Biometrics. Conventional Biometrics use only either physiological or behavioural characteristics. Physiological means “way the individual possesses” like fingerprint, iris scan and behavioural means “way the individual behaves” like signature, voice. Cognitive Biometrics is the biometric traits detected during cognitive or emotional brain states. Cognitive biometrics based on the Measurement of signals directly or indirectly generated by “Way the individual thinks”.
1
Corresponding author. Tel.: 07034482134; E-mail address: [email protected] .
2212-0173 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of RAEREST 2016 doi:10.1016/j.protcy.2016.08.103
K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
Recognition systems [3] are mainly classified into three fundamental classes, namely knowledge based, token based, and biometric based. A knowledge-based approach depends on “something you know” such as personal identification number (PIN), textual password. Token-based approaches are based on “something you have” such as smart card, passport. These two approaches have several drawbacks- tokens may be forgotten, lost, misplaced or stolen. The password can be guessed by an intruder or forgotten by an authenticated individual. The third approach, Biometrics are based on “something you are” such as fingerprint. Biometric uses physiological or behavioural features of an individual for recognition and it cannot be stolen or lost. Biometric techniques prevent unauthorized access to or fraudulent use of ATMs, Smart cards, Computer networks. Here introduce Brain waves [4] are used for biometric identifier. Brain waves are used for recognition purpose is a type of cognitive Biometrics. Brain is used to monitors and regulates the actions and reactions of the body. It acts as a controller for the central nervous system. Brain activity can be recorded either by measuring the blood flow in the brain or by measuring the neurons electrical activity. The Emotiv EPOC EEG neuro headset [5] has 14 saline electrodes [10] with two reference sensors is used for the acquisition of brainwaves. Nomenclature BCI EEG
Brain Computer Interface Electro Encephalogram
2. General Methodology Any biometric systems have two basic modes of operation. Verification and identification. First, in verification mode the system performs a one-to-one comparison of a captured biometric feature with a specific template stored in a biometric database in order to verify the individual is an authenticated person. Three steps are involved in the verification process. 1. Reference models are generated and stored in the model database 2. Samples are matched with reference models to generate the genuine and impostor scores and calculate the threshold value 3. Testing step: use a ID number to indicate which template should be used for comparison. Second, in identification mode the system performs a one-to-many comparison with a biometric database, to check the identity of an unknown individual. System will succeed in identifying the individual if the comparison of the biometric sample to a template in the database falls within a previously set threshold. General biometric system in Fig 1: contains: • Sensor: Is used to acquire all the necessary data • Enrolment: Is the first time of an individual uses a biometric system. During this phase, biometric information from an individual is captured and stored. • Pre-processing: In this step remove artifacts from the sensor • Feature extractor: In which necessary features need to be extracted in the optimal way • Template generator: A template is a synthesis of the relevant characteristics extracted from the source. • Matcher: In this phase the obtained template is passed to a matcher that compares it with other existing templates, estimating the distance between them using algorithm like hamming code.
241
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K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
Fig 1: General Biometric System
3. Brain Biometric Brain waves are used for biometric identifier. Brain is used to monitors and regulates the actions and reactions of the body. Brain activity can be recorded either by measuring the blood flow in the brain or by measuring the neurons electrical activity
Fig 2: Brain Biometric System 3.1. Brain
Wave Acquisition:
Electroencephalogram (EEG) is sensitive to the electrical field generated by the electric currents in the brain .EEG recordings are acquired with portable and relatively inexpensive devices. In the acquisition setup surface electrodes are placed which limits the number of the electrodes positioning on the whole scalp. EEG has 5 frequency
K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
components: Delta [0.5 - 4Hz] , Theta [4 – 8 Hz] , Alpha [8 – 14 Hz], Beta [14 – 30 Hz] ,Gamma [ > 30 Hz].From these components Slow waves are Delta & Theta and Fast waves are Beta & Gamma.
Fig 3: Brain Waves
The Emotive EEG headset [10] is a valid device for measuring EEG and evaluating the subjective emotional parameters of subjects and has a high level of reliability.We used the TestBench™ research software packet included in the Emotiv Research Edition SDK which provided the recording option of the EEG data files in binary EEGLAB format. For the experiment we have synchronized the visual output of pictures with an EEG data recording.
Fig 4: Channel selection
243
244
K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
The EEG signal pre-processing, by which means the raw EEG data [6] is prepared for the further analysis and feature extraction. The preparation usually consists of the following steps: band pass filtering, baseline removal, detrending, artifact removal and finally enhancing the useful EEG data using Independent Component Analysis. 3.2. Feature
Extraction:
Features extracted from Brain signals using various Techniques like Analysis based on time, Analysis based on frequency, Time-Frequency Analysis and Time-Frequency-Space Analysis, Wavelet Transform. Wavelet transform [7] is used to converts a signal into a different form revealing the characteristics hidden in the original signal. Wavelet is a small oscillating wave like characteristic and has its energy concentrated in time. Wavelet transform enables variable window sizes in analysing different frequency components within a signal. 3.3. Classification:
Extracted EEG [8] features are classified using an artificial neural network trained with the back propagation algorithm. Classification using a mapping function f, where f is got from a training set T, and to find the label of a feature vector x. 3.4. Brain
Computer Interface:
BCI [9] is a interface between brain and machine .BCI extracts electrical signals from brain and process them to generate control signals for computers. 4. Back end flow diagram The back-end part consists of two main modules: 1) the Signal Acquisition and Pre-processing module and 2) the Feature Extraction and Classification module. The advantage of such division is that the system can adapt to any EEG hardware by adjusting only the Signal Acquisition and Pre-processing module. The main purpose of this module is to deliver the selected raw EEG data in a compact and organized form to the Feature Extraction and Classification module, which is responsible for calculating and delivering the final authentication results to the front-end. The Flowchart [11] representing the EEG signal handling steps divided in two main modules. The first module represents the actual signal acquisition module which should be implemented on the particular environment connected directly to the EEG measuring device. The second part is responsible for feature extraction and matching the extracted features to the database records on the server side. 4.1. The
signal acquisition and pre-processing module:
This module built with a help from the OpenVIBE11 software platform [12], which is dedicated to designing, testing and using brain-computer interfaces. The most significant features can be extracted from the visual parietal-occipital cortex of the brain. Therefore the Channel Selector is created and is set to obtain the data from the following four EEG sensor locations: P7, P8, O1, O2, based on the international 10-20 system . The Butterworth Bandpass Filter [16] was applied in order to avoid frequencies which are lower than 0.5 Hz and higher than 40 Hz, because these frequences were not informative enough for further feature extraction . The CSV Package File Writer is responsible for generating an EEG data package by the request of the front-end. The numeric EEG measurement values are separated by commas, thus using a Comma Separated Value (CSV) syntax.
245
K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
Fig 5: Back end flow diagram
TIME(S),
P7,
O1,
O2,
P8,
0.000000, 0.007813, 0.015625, 0.023438, 0.031250, […]
4614.358887, 4621.025879, 4626.153809, 4614.358887, 4608.205078,
3870.769287, 3871.794922, 3862.051270, 3861.025635, 3861.538574,
4448.205078, 4457.436035, 4449.743652, 4447.692383, 4448.717773,
4300.512695, 4304.102539, 4295.897461, 4295.384766, 4295.384766,
SAMPLING RATE 128
Fig 6: EEG data packages 4.2. The
feature extraction and classification module:
This module consists of file verification, Detrending and the Baseline Removal [13], Feature Extractor and Feature Matcher. The package file verification procedure is necessary to ensure that the signal is acquired correctly and that it can be further used for the extraction of unique biometric features. To remove the baseline of the raw EEG dataset [14] for each electrode measurements individually, this means to remove the mean of the recording. This step ensures that the signal will be distributed around zero.
246
K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
The Design of database consists of 4 main tables: 'user_profile', 'password_images', 'eeg_features' and 'sensors'. The 'eeg_features' table is responsible for storing the unique subjects' EEG features which are used as biometric identifiers for classifying the individual. The mean base-line values, ZCR measures[15], Crosscorrelation, coherence values, PSD histograms, and finally latency values are stored in this table.
Fig 7: Dataflow
5. Comparison Analysis Table 1: Comparison of various biometrics
K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
6. Conclusion This paper presents how brain biometric used for user recognition. Biometrics is reliable way for identification, based on behavioural or physiological characteristics of an individual. Brain Biometric provides highest degree of recognition. Brain has enormous features (uniqueness, robustness to spoofing attacks, continuous identification and never produce duplicate signal) in biometrics to enhance diverse security levels and more reliable than other biometric technologies. It used to develop some new techniques and methodologies in lie detection, crime detection and to detect the record of specific terrorist act. Any incident stored in the brain and to intensify brain related methodologies in future. References [1] A. Ross, Salil Prabhakar and A. K. Jain, 2004 “An Introduction to Biometric Recognition”. IEEE Transaction on Circuits and Systems for Video Technology. [2] A.K. Jain, A.A. Ross, and K. Nandakumar, “Introduction to Biometrics,” Handbook of Biometrics, Springer, 2011, pp. 1–22. [3] A. Jain, L. Hong, and S. Pankanti, “Biometric identification,” Commun. ACM, vol. 43, Feb. 2000, pp. 90–98. [4] S. Akben, A. Subasi, and D. Tuncel, “Analysis of EEG Signals Under Flash Stimulation for Migraine and Epileptic Patients,” Journal of Medical Systems, vol. 35, Jun. 2011, pp. 437–443. [5] J. Rønager, Interview and Meeting about EEG and authentication., Aalborg University Copenhagen: 2012. [6] Anil K. Jain, KarthikNandakumar, and Abhishek Nagar “Biometric Template Security” Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008. [7] J. Thorpe, P.C. van Oorschot, and A. Somayaji, “Pass-thoughts: authenticating with our minds,” Proceedings of the 2005 workshop on New security paradigms, New York, NY, USA: ACM, 2005, pp. 45–56. [8] A. Zúquete, B. Quintela, and J.P.S. Cunha, “Biometric Authentication using Brain Responses to Visual Stimuli.,” BIOSIGNALS, Aveiro, Portugal: Institute of Electronics and Telematics Engineering of Aveiro , 2010, pp. 103–112. [9] C.Ashby,A.Bhatia, F.Tenore, and J. Vogelstein, “Low-cost electroencephalogram based authentication,” 2011 5th International IEEE/EMBS Conference n Neural Engineering (NER), IEEE, 2011, pp. 442–445. [10] D. Cernea, P.-S. Olech, A. Ebert, and A. Kerren, “EEG-Based Measurement of Subjective Parameters in Evaluations,” HCI International 2011 – Posters’ Extended Abstracts, C. Stephanidis, ed., Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 279–283 [11] Juris Klonovs, Henning Olesen, and Allan Hammershoj ,“ID Proof on the Go:Development of a Mobile EEG-Based Feature Extraction and Classification System for Biometric Authentication”, : Aalborg University Copenhagen, 2011 [12] Patrizio Campisi , Daria La Rocca “Brain Waves for Automatic Biometric-Based User Recognition” IEEE Transaction on Information Forensics and Security, Vol. 9, No. 5, May 2014. [13] Y. Boytsova and S. Danko, “EEG differences between resting states with eyes open and closed in darkness,” Human Physiol., vol. 36, no. 3, pp. 367–369, 2010 [14] T. Hinterberger et al., “Brain-computer communication and slow cortical potentials,” IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1011–1018, Jun. 2004 [15] S. L. Bressler, “Event-related potentials of the cerebral cortex,” in Electrophysiological Recording Tech., vol. 54, R. Neuromethods, P. Vertes, and R. W. Stackman, Eds., New York, NY, USA: Humana Press, 2011, pp. 169–190. [16] Palaniappan, R: “A new method to identify individuals using VEP signals and neural network”. IEE Proceedings - Science, Measurement and Technology Journal, Vol. 151, 2004, No: 16-20.
247
ScienceDirect Procedia Technology 25 (2016) 240 – 247
Global Colloquium in Recent Advancement and Effectual Researches in Engineering, Science and Technology (RAEREST 2016)
A Novel Approach to Brain Biometric User Recognition Reshmi K.C.1, Ihsana Muhammed P., Priya V.V., Akhila V.A. 1
Dept of Computer Science &Engineering, Royal College of Engineering & Technology, Chiramanangad P.O., Thrissur, 680604, India [email protected], [email protected],[email protected],[email protected]
Abstract Advances in the field of information technology makes information security is a major part of it. To deal with security, Recognition plays an important role .Now a day the numbers of organizations are using automated person recognition systems to improve customer satisfaction, to enhance operating efficiency and to secure critical resources. The reliable biometric recognition systems to either confirm or determine the individual’s identity to servicing their requests. This paper gives a novel approach on user recognition using EEG signals of Brain. EEG is sensitive to the electrical field generated by the electric currents induced in the brain. EEG recordings are acquired with portable and relatively inexpensive devices when compared to the other brain imaging technique like fMRI. The Emotiv EPOC EEG neuro headset has 14 saline electrodes with two reference sensors is used for the acquisition of brainwaves. © 2016 2015The TheAuthors. Authors.Published Published Elsevier Ltd. © byby Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of RAEREST 2016. Peer-review under responsibility of the organizing committee of RAEREST 2016
Keywords: Brain waves; BCI; EEG. 1. Introduction The term Biometrics [1] originated from two Greek words bios=”life” and metron = “measure”. Biometrics deals with the automated recognition of individuals based on their behavioural and physical characteristics. Mainly two types of Biometrics are there. Conventional Biometrics [2] and Cognitive Biometrics. Conventional Biometrics use only either physiological or behavioural characteristics. Physiological means “way the individual possesses” like fingerprint, iris scan and behavioural means “way the individual behaves” like signature, voice. Cognitive Biometrics is the biometric traits detected during cognitive or emotional brain states. Cognitive biometrics based on the Measurement of signals directly or indirectly generated by “Way the individual thinks”.
1
Corresponding author. Tel.: 07034482134; E-mail address: [email protected] .
2212-0173 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of RAEREST 2016 doi:10.1016/j.protcy.2016.08.103
K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
Recognition systems [3] are mainly classified into three fundamental classes, namely knowledge based, token based, and biometric based. A knowledge-based approach depends on “something you know” such as personal identification number (PIN), textual password. Token-based approaches are based on “something you have” such as smart card, passport. These two approaches have several drawbacks- tokens may be forgotten, lost, misplaced or stolen. The password can be guessed by an intruder or forgotten by an authenticated individual. The third approach, Biometrics are based on “something you are” such as fingerprint. Biometric uses physiological or behavioural features of an individual for recognition and it cannot be stolen or lost. Biometric techniques prevent unauthorized access to or fraudulent use of ATMs, Smart cards, Computer networks. Here introduce Brain waves [4] are used for biometric identifier. Brain waves are used for recognition purpose is a type of cognitive Biometrics. Brain is used to monitors and regulates the actions and reactions of the body. It acts as a controller for the central nervous system. Brain activity can be recorded either by measuring the blood flow in the brain or by measuring the neurons electrical activity. The Emotiv EPOC EEG neuro headset [5] has 14 saline electrodes [10] with two reference sensors is used for the acquisition of brainwaves. Nomenclature BCI EEG
Brain Computer Interface Electro Encephalogram
2. General Methodology Any biometric systems have two basic modes of operation. Verification and identification. First, in verification mode the system performs a one-to-one comparison of a captured biometric feature with a specific template stored in a biometric database in order to verify the individual is an authenticated person. Three steps are involved in the verification process. 1. Reference models are generated and stored in the model database 2. Samples are matched with reference models to generate the genuine and impostor scores and calculate the threshold value 3. Testing step: use a ID number to indicate which template should be used for comparison. Second, in identification mode the system performs a one-to-many comparison with a biometric database, to check the identity of an unknown individual. System will succeed in identifying the individual if the comparison of the biometric sample to a template in the database falls within a previously set threshold. General biometric system in Fig 1: contains: • Sensor: Is used to acquire all the necessary data • Enrolment: Is the first time of an individual uses a biometric system. During this phase, biometric information from an individual is captured and stored. • Pre-processing: In this step remove artifacts from the sensor • Feature extractor: In which necessary features need to be extracted in the optimal way • Template generator: A template is a synthesis of the relevant characteristics extracted from the source. • Matcher: In this phase the obtained template is passed to a matcher that compares it with other existing templates, estimating the distance between them using algorithm like hamming code.
241
242
K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
Fig 1: General Biometric System
3. Brain Biometric Brain waves are used for biometric identifier. Brain is used to monitors and regulates the actions and reactions of the body. Brain activity can be recorded either by measuring the blood flow in the brain or by measuring the neurons electrical activity
Fig 2: Brain Biometric System 3.1. Brain
Wave Acquisition:
Electroencephalogram (EEG) is sensitive to the electrical field generated by the electric currents in the brain .EEG recordings are acquired with portable and relatively inexpensive devices. In the acquisition setup surface electrodes are placed which limits the number of the electrodes positioning on the whole scalp. EEG has 5 frequency
K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
components: Delta [0.5 - 4Hz] , Theta [4 – 8 Hz] , Alpha [8 – 14 Hz], Beta [14 – 30 Hz] ,Gamma [ > 30 Hz].From these components Slow waves are Delta & Theta and Fast waves are Beta & Gamma.
Fig 3: Brain Waves
The Emotive EEG headset [10] is a valid device for measuring EEG and evaluating the subjective emotional parameters of subjects and has a high level of reliability.We used the TestBench™ research software packet included in the Emotiv Research Edition SDK which provided the recording option of the EEG data files in binary EEGLAB format. For the experiment we have synchronized the visual output of pictures with an EEG data recording.
Fig 4: Channel selection
243
244
K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
The EEG signal pre-processing, by which means the raw EEG data [6] is prepared for the further analysis and feature extraction. The preparation usually consists of the following steps: band pass filtering, baseline removal, detrending, artifact removal and finally enhancing the useful EEG data using Independent Component Analysis. 3.2. Feature
Extraction:
Features extracted from Brain signals using various Techniques like Analysis based on time, Analysis based on frequency, Time-Frequency Analysis and Time-Frequency-Space Analysis, Wavelet Transform. Wavelet transform [7] is used to converts a signal into a different form revealing the characteristics hidden in the original signal. Wavelet is a small oscillating wave like characteristic and has its energy concentrated in time. Wavelet transform enables variable window sizes in analysing different frequency components within a signal. 3.3. Classification:
Extracted EEG [8] features are classified using an artificial neural network trained with the back propagation algorithm. Classification using a mapping function f, where f is got from a training set T, and to find the label of a feature vector x. 3.4. Brain
Computer Interface:
BCI [9] is a interface between brain and machine .BCI extracts electrical signals from brain and process them to generate control signals for computers. 4. Back end flow diagram The back-end part consists of two main modules: 1) the Signal Acquisition and Pre-processing module and 2) the Feature Extraction and Classification module. The advantage of such division is that the system can adapt to any EEG hardware by adjusting only the Signal Acquisition and Pre-processing module. The main purpose of this module is to deliver the selected raw EEG data in a compact and organized form to the Feature Extraction and Classification module, which is responsible for calculating and delivering the final authentication results to the front-end. The Flowchart [11] representing the EEG signal handling steps divided in two main modules. The first module represents the actual signal acquisition module which should be implemented on the particular environment connected directly to the EEG measuring device. The second part is responsible for feature extraction and matching the extracted features to the database records on the server side. 4.1. The
signal acquisition and pre-processing module:
This module built with a help from the OpenVIBE11 software platform [12], which is dedicated to designing, testing and using brain-computer interfaces. The most significant features can be extracted from the visual parietal-occipital cortex of the brain. Therefore the Channel Selector is created and is set to obtain the data from the following four EEG sensor locations: P7, P8, O1, O2, based on the international 10-20 system . The Butterworth Bandpass Filter [16] was applied in order to avoid frequencies which are lower than 0.5 Hz and higher than 40 Hz, because these frequences were not informative enough for further feature extraction . The CSV Package File Writer is responsible for generating an EEG data package by the request of the front-end. The numeric EEG measurement values are separated by commas, thus using a Comma Separated Value (CSV) syntax.
245
K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
Fig 5: Back end flow diagram
TIME(S),
P7,
O1,
O2,
P8,
0.000000, 0.007813, 0.015625, 0.023438, 0.031250, […]
4614.358887, 4621.025879, 4626.153809, 4614.358887, 4608.205078,
3870.769287, 3871.794922, 3862.051270, 3861.025635, 3861.538574,
4448.205078, 4457.436035, 4449.743652, 4447.692383, 4448.717773,
4300.512695, 4304.102539, 4295.897461, 4295.384766, 4295.384766,
SAMPLING RATE 128
Fig 6: EEG data packages 4.2. The
feature extraction and classification module:
This module consists of file verification, Detrending and the Baseline Removal [13], Feature Extractor and Feature Matcher. The package file verification procedure is necessary to ensure that the signal is acquired correctly and that it can be further used for the extraction of unique biometric features. To remove the baseline of the raw EEG dataset [14] for each electrode measurements individually, this means to remove the mean of the recording. This step ensures that the signal will be distributed around zero.
246
K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
The Design of database consists of 4 main tables: 'user_profile', 'password_images', 'eeg_features' and 'sensors'. The 'eeg_features' table is responsible for storing the unique subjects' EEG features which are used as biometric identifiers for classifying the individual. The mean base-line values, ZCR measures[15], Crosscorrelation, coherence values, PSD histograms, and finally latency values are stored in this table.
Fig 7: Dataflow
5. Comparison Analysis Table 1: Comparison of various biometrics
K.C. Reshmi et al. / Procedia Technology 25 (2016) 240 – 247
6. Conclusion This paper presents how brain biometric used for user recognition. Biometrics is reliable way for identification, based on behavioural or physiological characteristics of an individual. Brain Biometric provides highest degree of recognition. Brain has enormous features (uniqueness, robustness to spoofing attacks, continuous identification and never produce duplicate signal) in biometrics to enhance diverse security levels and more reliable than other biometric technologies. It used to develop some new techniques and methodologies in lie detection, crime detection and to detect the record of specific terrorist act. Any incident stored in the brain and to intensify brain related methodologies in future. References [1] A. Ross, Salil Prabhakar and A. K. Jain, 2004 “An Introduction to Biometric Recognition”. IEEE Transaction on Circuits and Systems for Video Technology. [2] A.K. Jain, A.A. Ross, and K. Nandakumar, “Introduction to Biometrics,” Handbook of Biometrics, Springer, 2011, pp. 1–22. [3] A. Jain, L. Hong, and S. Pankanti, “Biometric identification,” Commun. ACM, vol. 43, Feb. 2000, pp. 90–98. [4] S. Akben, A. Subasi, and D. Tuncel, “Analysis of EEG Signals Under Flash Stimulation for Migraine and Epileptic Patients,” Journal of Medical Systems, vol. 35, Jun. 2011, pp. 437–443. [5] J. Rønager, Interview and Meeting about EEG and authentication., Aalborg University Copenhagen: 2012. [6] Anil K. Jain, KarthikNandakumar, and Abhishek Nagar “Biometric Template Security” Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008. [7] J. Thorpe, P.C. van Oorschot, and A. Somayaji, “Pass-thoughts: authenticating with our minds,” Proceedings of the 2005 workshop on New security paradigms, New York, NY, USA: ACM, 2005, pp. 45–56. [8] A. Zúquete, B. Quintela, and J.P.S. Cunha, “Biometric Authentication using Brain Responses to Visual Stimuli.,” BIOSIGNALS, Aveiro, Portugal: Institute of Electronics and Telematics Engineering of Aveiro , 2010, pp. 103–112. [9] C.Ashby,A.Bhatia, F.Tenore, and J. Vogelstein, “Low-cost electroencephalogram based authentication,” 2011 5th International IEEE/EMBS Conference n Neural Engineering (NER), IEEE, 2011, pp. 442–445. [10] D. Cernea, P.-S. Olech, A. Ebert, and A. Kerren, “EEG-Based Measurement of Subjective Parameters in Evaluations,” HCI International 2011 – Posters’ Extended Abstracts, C. Stephanidis, ed., Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 279–283 [11] Juris Klonovs, Henning Olesen, and Allan Hammershoj ,“ID Proof on the Go:Development of a Mobile EEG-Based Feature Extraction and Classification System for Biometric Authentication”, : Aalborg University Copenhagen, 2011 [12] Patrizio Campisi , Daria La Rocca “Brain Waves for Automatic Biometric-Based User Recognition” IEEE Transaction on Information Forensics and Security, Vol. 9, No. 5, May 2014. [13] Y. Boytsova and S. Danko, “EEG differences between resting states with eyes open and closed in darkness,” Human Physiol., vol. 36, no. 3, pp. 367–369, 2010 [14] T. Hinterberger et al., “Brain-computer communication and slow cortical potentials,” IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1011–1018, Jun. 2004 [15] S. L. Bressler, “Event-related potentials of the cerebral cortex,” in Electrophysiological Recording Tech., vol. 54, R. Neuromethods, P. Vertes, and R. W. Stackman, Eds., New York, NY, USA: Humana Press, 2011, pp. 169–190. [16] Palaniappan, R: “A new method to identify individuals using VEP signals and neural network”. IEE Proceedings - Science, Measurement and Technology Journal, Vol. 151, 2004, No: 16-20.
247