Cognitive Biometrics: Challenges for the Future

Cognitive biometrics is a novel approach to user authentication/identification which utilises a biosignal based approach. Specifically, current implementations rely on the use of the electroencephalogram (EEG), electrocardiogram (ECG), and the electrodermal response (EDR) as inputs into a traditional authentication scheme. The scientific basis for the deployment of biosignals resides principally on their uniqueness -for instance the theta power band in adults presents a phenotypic/genetic correlation of approximately 75%. The numbers are roughly the same for ECG, with an heritability correlation for the peak-to-peak (R-R interval) times of over 77%. For EDR, the results indicate that there is approximately a 50% heritability score (h2). The challenge with respect to cognitive biometrics based on biosignals is to enhance the information content of the acquired data.

[1]  Carles Grau,et al.  Unobtrusive Biometric System Based on Electroencephalogram Analysis , 2008, EURASIP J. Adv. Signal Process..

[2]  José del R. Millán,et al.  Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Raymond J Staron,et al.  Personal Attributes Authentication Techniques. , 1977 .

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

[5]  A. Waller A Demonstration on Man of Electromotive Changes accompanying the Heart's Beat , 1887, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[6]  Julie Thorpe,et al.  Graphical Dictionaries and the Memorable Space of Graphical Passwords , 2004, USENIX Security Symposium.

[7]  J J Vidal,et al.  Toward direct brain-computer communication. , 1973, Annual review of biophysics and bioengineering.

[8]  J. Russell A circumplex model of affect. , 1980 .

[9]  R.P.N. Rao,et al.  An Image-based Brain-Computer Interface Using the P3 Response , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[10]  Ramaswamy Palaniappan Multiple Mental Thought Parametric Classification: A New Approach for Individual Identification , 2008 .

[11]  Ramaswamy Palaniappan,et al.  Enhanced Detection of Visual-Evoked Potentials in Brain-Computer Interface Using Genetic Algorithm and Cyclostationary Analysis , 2007, Comput. Intell. Neurosci..

[12]  E. John,et al.  Evoked-Potential Correlates of Stimulus Uncertainty , 1965, Science.

[13]  Luís Paulo Reis,et al.  Biometric Emotion Assessment and Feedback in an Immersive Digital Environment , 2009, Int. J. Soc. Robotics.

[14]  Tommi Nykopp,et al.  Statistical Modelling Issues for The Adaptive Brain Interface , 2001 .

[15]  Marios Poulos,et al.  Neural network based person identification using EEG features , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

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

[17]  Dong-Jun Kim,et al.  A Robust Human Identification by Normalized Time-Domain Features of Electrocardiogram , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[18]  Brenda K. Wiederhold,et al.  ECG to identify individuals , 2005, Pattern Recognit..

[19]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[20]  Hong Xian,et al.  Stability, consistency, and heritability of electrodermal response lability in middle-aged male twins. , 2004, Psychophysiology.