A novel method for gaze tracking by local pattern model and support vector regressor

• Published in: Signal processing Vol. 90; no. 4; pp. 1290 - 1299
• Main Authors: Lu, Hu-Chuan, Fang, Guo-Liang, Wang, Chao, Chen, Yen-Wei
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.04.2010; Elsevier
• Subjects: Applied sciences; Exact sciences and technology; Eyes; Gaze tracking; Head movement; Information, signal and communications theory; LPM; Mathematical analysis; Mathematical models; Miscellaneous; PPBTF; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Surface layer; SVR; Telecommunications and information theory; Texture; Tracking; Vectors (mathematics); PPBTF; LPM; SVR; Gaze tracking; Texture analysis; Automatic classification; State of the art; Target tracking; Gaze; Mapping; Regression analysis; Support vector machine; Signal classification; Accuracy; Signal processing; Feature extraction; Binocular vision; Content analysis
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2009.10.014

This paper presents a novel eye gaze tracking method with allowable head movement based on a local pattern model (LPM) and support vector regressor (SVR). The LPM, a combination of improved pixel-pattern-based texture feature (PPBTF) and local-binary-pattern texture feature (LBP), is employed to calculate texture features from the characteristics of the eyes and a new binocular vision scheme is adopted to detect the spatial coordinates of the eyes. The texture features from LPM and the spatial coordinates together are fed into support vector regressor (SVR) to match a gaze mapping function, and subsequently to track gaze direction under allowable head movement. The experimental results show that the proposed approach results in better accuracy in estimating the gaze direction than the state-of-the-art pupil center corneal reflection (PCCR) method.