Using Dissimilarity Matrix for Eye Movement Biometrics with a Jumping Point Experiment

The paper presents studies on the application of the dissimilarity matrix-based method to the eye movement analysis. This method was utilized in the biometric identification task. To assess its efficiency four different datasets based on similar scenario (‘jumping point’ type) yet using different eye trackers, recording frequencies and time intervals have been used. It allowed to build the common platform for the research and to draw some interesting comparisons. The dissimilarity matrix, which has never been used for identifying people on the basis of their eye movements, was constructed with usage of different distance measures. Additionally, there were different signal transforms and metrics checked and their performance on various datasets was compared. It is worth mentioning that the paper presents the algorithm that was used during the BioEye 2015 competition and ranked as one of the top three methods.

[1]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[2]  Pawel Kasprowski,et al.  The influence of dataset quality on the results of behavioral biometric experiments , 2013, 2013 International Conference of the BIOSIG Special Interest Group (BIOSIG).

[3]  Robert P. W. Duin,et al.  The dissimilarity space: Bridging structural and statistical pattern recognition , 2012, Pattern Recognit. Lett..

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

[5]  Pawel Kasprowski,et al.  First eye movement verification and identification competition at BTAS 2012 , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[6]  Oleg V. Komogortsev,et al.  Complex eye movement pattern biometrics: Analyzing fixations and saccades , 2013, 2013 International Conference on Biometrics (ICB).

[7]  Pawel Kasprowski,et al.  The Impact of Temporal Proximity between Samples on Eye Movement Biometric Identification , 2013, CISIM.

[8]  Ioannis Rigas,et al.  Biometric identification based on the eye movements and graph matching techniques , 2012, Pattern Recognit. Lett..

[9]  Ioannis Rigas,et al.  BioEye 2015: Competition on biometrics via eye movements , 2015, 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[10]  Youtian Du,et al.  User Authentication Through Mouse Dynamics , 2013, IEEE Transactions on Information Forensics and Security.

[11]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[12]  Cecilia R. Aragon,et al.  Biometric identification via an oculomotor plant mathematical model , 2010, ETRA.

[13]  Cecilia R. Aragon,et al.  Biometric authentication via oculomotor plant characteristics , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

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

[15]  Katarzyna Harezlak,et al.  The Second Eye Movements Verification and Identification Competition , 2014, IEEE International Joint Conference on Biometrics.

[16]  Pawel Kasprowski,et al.  Enhancing eye-movement-based biometric identification method by using voting classifiers , 2005, SPIE Defense + Commercial Sensing.