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.