Biometric authentication via oculomotor plant characteristics

A novel biometrics approach that performs authentication via the internal non-visible anatomical structure of an individual human eye is proposed and evaluated. To provide authentication, the proposed method estimates the anatomical characteristics of the oculomotor plant (comprising the eye globe, its muscles and the brain's control signals). The estimation of the oculomotor plant characteristics (OPC) is achieved by analyzing the recorded eye movement trajectories via a 2D linear homeomorphic mathematical representation of the oculomotor plant. The derived OPC allow authentication via various statistical methods and information fusion techniques. The proposed authentication method yielded Half Total Error Rate of 19% for a pool of 59 recorded subjects in the best case. The OPC biometric authentication has high counterfeit resistance potential, because it includes both behavioral and physiological human attributes that are hard to reproduce.

[1]  M. K. Khan,et al.  Machine identification of human faces , 1981, Pattern Recognition.

[2]  Ching Y. Suen,et al.  Application of majority voting to pattern recognition: an analysis of its behavior and performance , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[3]  N. Shimizu.  [Neurology of eye movements]. , 2000, Rinsho shinkeigaku = Clinical neurology.

[4]  Anil K. Jain,et al.  Periocular biometrics in the visible spectrum: A feasibility study , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[5]  Tomi Kinnunen,et al.  Eye-Movements as a Biometric , 2005, SCIA.

[6]  Oleg V. Komogortsev,et al.  Eye movement prediction by Kalman filter with integrated linear horizontal oculomotor plant mechanical model , 2008, ETRA.

[7]  Oleg V. Komogortsev,et al.  Standardization of Automated Analyses of Oculomotor Fixation and Saccadic Behaviors , 2010, IEEE Transactions on Biomedical Engineering.

[8]  Richard J. Harris.  A primer of multivariate statistics , 1975 .

[9]  H. Hotelling.  The Generalization of Student’s Ratio , 1931 .

[10]  G. Box,et al.  A general distribution theory for a class of likelihood criteria. , 1949, Biometrika.

[11]  Oleg V. Komogortsev,et al.  2D Oculomotor Plant Mathematical Model for eye movement simulation , 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.

[12]  Arun Ross,et al.  Handbook of Multibiometrics , 2006, The Kluwer international series on biometrics.

[13]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[14]  Sharath Pankanti,et al.  BIOMETRIC IDENTIFICATION , 2000 .

[15]  K.W. Bowyer,et al.  All Iris Code Bits are Not Created Equal , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[16]  Pawel Kasprowski,et al.  Eye Movements in Biometrics , 2004, ECCV Workshop BioAW.

[17]  N. Balakrishnan,et al.  A Primer on Statistical Distributions , 2003 .

[18]  G. Aguilar,et al.  Multimodal biometric system using fingerprint , 2007, 2007 International Conference on Intelligent and Advanced Systems.

[19]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[20]  Robert L. Mason,et al.  Statistical Principles in Experimental Design , 2003 .

[21]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  William J. Schull,et al.  On the robustness of the T02 test in multivariate analysis of variance when variance-covariance matrices are not equal , 1964 .

[23]  John Michael Williams.  Biometrics or ... biohazards? , 2002, NSPW '02.