Please use this identifier to cite or link to this item:
Title: Video-based biometric identification using eye tracking technique
Authors: Liang, Z
Tan, F
Chi, Z 
Keywords: Biometric identification
Video-based eye tracking
Visual attention characteristics
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC), 12-15 August 2012, Hong Kong, p. 728-733 How to cite?
Abstract: Recently, biometric identification techniques have attracted great attention due to increasing demand of high-performance security systems. Compared with conventional identification methods, biometric techniques provide more reliable and robust solutions. In this paper, a novel video-based biometric identification model based on eye tracking technique is proposed. Inspired by visual attention, video clips are designed for subjects to view in order to capture eye tracking data reflecting their physiological and behavioral characteristics. Various visual attention characteristics, including acceleration, geometric, and muscle properties, are extracted from eye gaze data and used as biometric features to identify persons. An algorithm based on mutual information of features is adopted to perform feature evaluation for obtaining a set of the most discriminative features for biometric identification. Experiments are conducted by using two types of classifiers, Back-Propagation (BP) neural network and Support Vector Machine (SVM). Experimental results show that using video-based eye tracking data for biometric identification is feasible. In particular, eye tracking can be used as an additional biometric modal to enhance the performance of current biometric person identification systems.
ISBN: 978-1-4673-2192-1
DOI: 10.1109/ICSPCC.2012.6335584
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Jul 29, 2018

Page view(s)

Last Week
Last month
Citations as of Aug 14, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.