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Mathematical Problems in Engineering
Volume 2014, Article ID 548791, 13 pages
Research Article

A Joint Learning Approach to Face Detection in Wavelet Compressed Domain

1Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 300, Taiwan
2Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan

Received 27 September 2013; Revised 16 January 2014; Accepted 30 January 2014; Published 11 March 2014

Academic Editor: Yue Wu

Copyright © 2014 Szu-Hao Huang and Shang-Hong Lai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Face detection has been an important and active research topic in computer vision and image processing. In recent years, learning-based face detection algorithms have prevailed with successful applications. In this paper, we propose a new face detection algorithm that works directly in wavelet compressed domain. In order to simplify the processes of image decompression and feature extraction, we modify the AdaBoost learning algorithm to select a set of complimentary joint-coefficient classifiers and integrate them to achieve optimal face detection. Since the face detection on the wavelet compression domain is restricted by the limited discrimination power of the designated feature space, the proposed learning mechanism is developed to achieve the best discrimination from the restricted feature space. The major contributions in the proposed AdaBoost face detection learning algorithm contain the feature space warping, joint feature representation, ID3-like plane quantization, and weak probabilistic classifier, which dramatically increase the discrimination power of the face classifier. Experimental results on the CBCL benchmark and the MIT + CMU real image dataset show that the proposed algorithm can detect faces in the wavelet compressed domain accurately and efficiently.