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Multi-view Face Detection Based on the Enhanced AdaBoost Using Walsh Features

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3 Author(s)
Yunyang Yan ; Nanjing Univ. of Sci. & Technol., Nanjing ; Zhibo Guo ; Jingyu Yang

A novel face detection algorithm is proposed in this paper to improve the training speed and detection performance. Firstly, we used Walsh features instead of Haar-like features in the AdaBoost algorithm. Walsh features have less redundancy than Haar-like features due to its orthogonal specialty. Then, we defined a kind of week classifiers with dual-threshold to speedup training process and increase accuracy. Furthermore, during training, dual-threshold of every classifier is adoptively adjusted to separate the face and non-face as far as possible. Experimental results on MIT+CMU frontal face set and CMU profile face set demonstrated that the proposed technique can achieve better results on the detection speed and accuracy than the corresponding method.

Published in:

Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on  (Volume:1 )

Date of Conference:

July 30 2007-Aug. 1 2007