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

Authors:
Yunyang Yan;Zhibo Guo;Jingyu Yang
Affiliations:
Nanjing University of Science and Technology, China/ Huaiyin Institute of Technology, China;Nanjing University of Science and Technology, China;Nanjing University of Science and Technology, China
Venue:
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01
Year:
2007

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A novel face detection method based on contourlet features

ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications

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Abstract

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 adaptively 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.