Affiliations: [a] Computer Faculty, Guangdong University of Technology, Guangzhou, Guangdong, China | [b] Biomedical Engineering Center, Beijing University of Technology, Beijing, China
Correspondence:
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Corresponding authors: Ling Zhang, Guangdong University of Technology, Guangzhou 510006, Guangdong, China. E-mail: [email protected]; Yanjun Zeng, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100022, China. E-mail: [email protected].
Abstract: Fatigue may lead to potential accidents, but its diagnosis is difficult so it is easy to be delayed or missed. In this paper, a novel 3D facial-image-based fatigue detection method is presented. There are three steps involved: First, 3D surface curvature-based methods are combined with some 2D image-based methods to locate the position of facial fatigue feature points on the facial 3D image-based model, which is reconstructed based on a pair of binocular facial images. Secondly, facial fatigue features, such as eye blink, gaze direction, mouth morphology, and facial expressions are extracted from the facial 3D image-based model by the corresponding extraction methods. Finally, together with N-times sampling data, the attribute value of each facial fatigue feature is calculated, and then integrated together to recognize fatigue by linear discriminant analysis (LDA) algorithm. The following experimental results show that compared with only 2D image-based method, the accurate location of facial feature points, attribute value's calculation, and fatigue's fusion detection are obviously improved using the method presented in this paper.
Keywords: Binocular vision, 3D point location, fatigue detection, facial fatigue feature, gaussian curvature, fusion recognition
