DocumentCode :
2626321
Title :
An Eye States Detection Method by Using WLBP
Author :
Fan Liu ; Jinhui Tang ; Zhenmin Tang
Author_Institution :
Sch. of Comput. Eng., Nanjing Univ. of Sci. & Technol., Nanjing, China
fYear :
2013
fDate :
16-18 Sept. 2013
Firstpage :
198
Lastpage :
201
Abstract :
In this paper, we presents an efficient eye states detection method by using WLBP. WLBP is a novel feature extraction method we proposed, which combines the advantages of WLD and LBP. For an eye image, its WLBP histogram is extracted and each bin of the histogram is regarded as a feature of the eye. By using the extracted eye features and Support Vector Machines (SVM), a non-linear classifier is trained to recognize the eye state. Experimental results show that WLBP is obviously superior to WLD and LBP. Meanwhile, it´s also robust to noise and light variation. The experiments under inside-car environment also demonstrate the effectiveness and robustness of our method.
Keywords :
feature extraction; gaze tracking; image classification; support vector machines; SVM; WLBP histogram; Weber local binary pattern; eye feature extraction; eye image; eye states detection method; feature extraction method; inside-car environment; light variation; nonlinear classifier; support vector machines; Face; Fatigue; Feature extraction; Histograms; Noise; Robustness; Support vector machines; LBP; SVM; WLBP; WLD;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Semantic Computing (ICSC), 2013 IEEE Seventh International Conference on
Conference_Location :
Irvine, CA
Type :
conf
DOI :
10.1109/ICSC.2013.42
Filename :
6693517
Link To Document :
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