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Proceedings Paper

Adaboost multi-view face detection based on YCgCr skin color model
Author(s): Qi Lan; Zhiyong Xu
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Paper Abstract

Traditional Adaboost face detection algorithm uses Haar-like features training face classifiers, whose detection error rate is low in the face region. While under the complex background, the classifiers will make wrong detection easily to the background regions with the similar faces gray level distribution, which leads to the error detection rate of traditional Adaboost algorithm is high. As one of the most important features of a face, skin in YCgCr color space has good clustering. We can fast exclude the non-face areas through the skin color model. Therefore, combining with the advantages of the Adaboost algorithm and skin color detection algorithm, this paper proposes Adaboost face detection algorithm method that bases on YCgCr skin color model. Experiments show that, compared with traditional algorithm, the method we proposed has improved significantly in the detection accuracy and errors.

Paper Details

Date Published: 27 September 2016
PDF: 5 pages
Proc. SPIE 9684, 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment, 96842D (27 September 2016); doi: 10.1117/12.2243232
Show Author Affiliations
Qi Lan, Institute of Optics and Electronics (China)
Univ. of Chinese Academy of Sciences (China)
Zhiyong Xu, Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 9684:
8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment
Yudong Zhang; Fan Wu; Ming Xu; Sandy To, Editor(s)

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