Copyright © 2006 The Institute of Electronics, Information and Communication Engineers
Regular Section -- Papers -- Image Recognition, Computer Vision |
A Multi-Stage Approach to Fast Face Detection
1 The authors are with The Graduate University for Advanced Studies (SOKENDAI), Tokyo, 101–8430 Japan. E-mail: ledduy{at}grad.nii.ac.jp, 2 The author is with the National Institute of Informatics (NII), Tokyo, 101–8430 Japan.
A multi-stage approach — which is fast, robust and easy to train — for a face-detection system is proposed. Motivated by the work of Viola and Jones [1], this approach uses a cascade of classifiers to yield a coarse-to-fine strategy to reduce significantly detection time while maintaining a high detection rate. However, it is distinguished from previous work by two features. First, a new stage has been added to detect face candidate regions more quickly by using a larger window size and larger moving step size. Second, support vector machine (SVM) classifiers are used instead of AdaBoost classifiers in the last stage, and Haar wavelet features selected by the previous stage are reused for the SVM classifiers robustly and efficiently. By combining AdaBoost and SVM classifiers, the final system can achieve both fast and robust detection because most non-face patterns are rejected quickly in earlier layers, while only a small number of promising face patterns are classified robustly in later layers. The proposed multi-stage-based system has been shown to run faster than the original AdaBoost-based system while maintaining comparable accuracy.
Key Words: fast object detection, face detection, AdaBoost, SVM, cascaded classifiers, Haar wavelet, multi-stage classification
Manuscript received October 31, 2005. Manuscript revised February 27, 2006.