A Novel Local Feature for Eye Movement Authentication

Eye movement authentication technology has been proposed as a biometric modality, which enables to authenticate a user continuously, and has the counterfeit feature because of the difficulty of the imitation. By using the eye movement authentication, it is possible to realize an automatic authentication system as long as he/she is looking at a display to operate the device. However, the authentication accuracy is still low compared to the other traditional biometric modalities, such as fingerprint, face, and iris. In this paper, we propose the novel local eye movement feature to represent local differences of the captured time-series gazing position data, and we show the proposed feature can work as a complement feature against Mel Frequency Cepstrum Coefficients(MFCC), which is based on the local phase information of eye movement data.We show the classification rate improves from 61% to 82% in the BioEye2015 dataset by using our proposed method on the best case.

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

[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]  Adel Hafiane,et al.  One dimensional local binary pattern for bone texture characterization , 2012, Pattern Analysis and Applications.

[4]  Hugo Proença,et al.  Multimodal ocular biometrics approach: A feasibility study , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[5]  Oleg V. Komogortsev,et al.  Complex Eye Movement Pattern Biometrics: The Effects of Environment and Stimulus , 2013, IEEE Transactions on Information Forensics and Security.

[6]  Adilson Gonzaga,et al.  Dynamic Features for Iris Recognition , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  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).

[8]  Oleg V Komogortsev,et al.  Automated classification and scoring of smooth pursuit eye movements in the presence of fixations and saccades , 2013, Behavior research methods.

[9]  Oleg V. Komogortsev,et al.  Liveness detection via oculomotor plant characteristics: Attack of mechanical replicas , 2013, 2013 International Conference on Biometrics (ICB).

[10]  Oleg V. Komogortsev,et al.  CUE: counterfeit-resistant usable eye movement-based authentication via oculomotor plant characteristics and complex eye movement patterns , 2012, Defense + Commercial Sensing.

[11]  Tomi Kinnunen,et al.  Towards task-independent person authentication using eye movement signals , 2010, ETRA.

[12]  Ioannis Rigas,et al.  Human eye movements as a trait for biometrical identification , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[13]  Joseph H. Goldberg,et al.  Identifying fixations and saccades in eye-tracking protocols , 2000, ETRA.

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

[15]  Vu C. Dinh,et al.  Mel-frequency Cepstral Coefficients for Eye Movement Identification , 2012, 2012 IEEE 24th International Conference on Tools with Artificial Intelligence.

[16]  Arun Ross,et al.  Periocular Biometrics in the Visible Spectrum , 2011, IEEE Transactions on Information Forensics and Security.