Ms. Liting Wang Profile

Liting Wang

at Tsinghua Univ

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Proceedings Article | 11 February 2009 Paper

Eye blink detection based on eye contour extraction

Liting Wang, Xiaoqing Ding, Chi Fang, Changsong Liu, Kongqiao Wang

Proceedings Volume 7245, 72450R (2009) https://doi.org/10.1117/12.804916

KEYWORDS: Eye, Facial recognition systems, Eye models, Lead, Detection and tracking algorithms, Databases, Video, Glasses, Statistical modeling, Machine vision

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Eye blink detection is one of the important problems in computer vision. It has many applications such as face live detection and driver fatigue analysis. The existing methods towards eye blink detection can be roughly divided into two categories: contour template based and appearance based methods. The former one usually can extract eye contours accurately. However, different templates should be involved for the closed and open eyes separately. These methods are also sensitive to illumination changes. In the appearance based methods, image patches of open-eyes and closed-eyes are collected as positive and negative samples to learn a classifier, but eye contours can not be accurately extracted. To overcome drawbacks of the existing methods, this paper proposes an effective eye blink detection method based on an improved eye contour extraction technique. In our method, eye contour model is represented by 16 landmarks therefore it can describe both open and closed eyes. Each landmark is accurately recognized by fast classifier which is trained from the appearance around this landmark. Experiments have been conducted on YALE and another large data set consisting of frontal face images to extract the eye contour. The experimental results show that the proposed method is capable of affording accurate eye location and robust in closed eye condition. It also performs well in the case of illumination variants. The average time cost of our method is about 140ms on Pentium IV 2.8GHz PC 1G RAM, which satisfies the real-time requirement for face video sequences. This method is also applied in a face live detection system and the results are promising.

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