Biometrics from Brain Electrical Activity: A Machine Learning Approach

The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the visual evoked potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis

[1]  Richard P. Wildes,et al.  Iris recognition: an emerging biometric technology , 1997, Proc. IEEE.

[2]  P. Nus,et al.  Adaptive spectral analysis of sleep spindles based on subspace tracking , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Vassilios Chrissikopoulos,et al.  Parametric person identification from the EEG using computational geometry , 1999, ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357).

[4]  Danilo P. Mandic,et al.  Energy of Brain Potentials Evoked During Visual Stimulus: A New Biometric? , 2005, ICANN.

[5]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[6]  Anil K. Jain,et al.  Biometric Systems: Technology, Design and Performance Evaluation , 2004 .

[7]  R. P. Wildes.  Iris recognition : An emerging biometric technology : Automated biometrics , 1997 .

[8]  Chulhan Lee,et al.  Online signature verification using temporal shift estimated by the phase of Gabor filter , 2005 .

[9]  Z. Korotkaya.  Biometric Person Authentication: Odor , 2003 .

[10]  J. G. Snodgrass,et al.  A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity. , 1980, Journal of experimental psychology. Human learning and memory.

[11]  David J. Hurley,et al.  Force Field Feature Extraction for Ear Biometrics , 2005, Image Pattern Recognition.

[12]  Ola Pettersson,et al.  ECG analysis: a new approach in human identification , 2001, IEEE Trans. Instrum. Meas..

[13]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[14]  L. Benedicenti,et al.  The electroencephalogram as a biometric , 2001, Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555).

[15]  N. Di Renzo,et al.  Enhanced high resolution spectral analysis of sleep spindles , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  K. Jellinger,et al.  Memory and Brain Dynamics Oscillations Integrating Attention, Perception, Learning, and Memory , 2005 .

[17]  Metin Akay,et al.  Detection and Estimation Methods for Biomedical Signals , 1996 .

[18]  Ramaswamy Palaniappan,et al.  Individual identification technique using visual evoked potential signals , 2002 .

[19]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[20]  Anil K. Jain,et al.  Matching of palmprints , 2002, Pattern Recognit. Lett..

[21]  Kohji Fukunaga,et al.  Introduction to Statistical Pattern Recognition-Second Edition , 1990 .

[22]  Ana González-Marcos,et al.  Biometric Identification through Hand Geometry Measurements , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  William J. J. Roberts,et al.  Speaker classification using composite hypothesis testing and list decoding , 2005, IEEE Transactions on Speech and Audio Processing.

[24]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[25]  H. Begleiter,et al.  Event related potentials during object recognition tasks , 1995, Brain Research Bulletin.

[26]  Ashok A. Ghatol,et al.  Iris recognition: an emerging biometric technology , 2007 .

[27]  Ramaswamy Palaniappan,et al.  Method of identifying individuals using VEP signals and neural network , 2004 .

[28]  Gürsel Serpen,et al.  Passphrase Authentication Based on Typing Style Through an Art 2 Neural Network , 2002, Int. J. Comput. Intell. Appl..

[29]  Anil K. Jain,et al.  Handbook of Fingerprint Recognition , 2005, Springer Professional Computing.

[30]  Vassilios Chrissikopoulos,et al.  Person identification based on parametric processing of the EEG , 1999, ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357).