CUED Publications database

Mel-frequency cepstral coefficients for eye movement identification

Cuong, NV and Dinh, V and Ho, LST (2012) Mel-frequency cepstral coefficients for eye movement identification. In: UNSPECIFIED pp. 253-260..

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Human identification is an important task for various activities in society. In this paper, we consider the problem of human identification using eye movement information. This problem, which is usually called the eye movement identification problem, can be solved by training a multiclass classification model to predict a person's identity from his or her eye movements. In this work, we propose using Mel-frequency cepstral coefficients (MFCCs) to encode various features for the classification model. Our experiments show that using MFCCs to represent useful features such as eye position, eye difference, and eye velocity would result in a much better accuracy than using Fourier transform, cepstrum, or raw representations. We also compare various classification models for the task. From our experiments, linear-kernel SVMs achieve the best accuracy with 93.56% and 91.08% accuracy on the small and large datasets respectively. Besides, we conduct experiments to study how the movements of each eye contribute to the final classification accuracy. © 2012 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:37
Last Modified: 22 May 2018 08:04