Face Detection Based on AdaBoost Algorithm with Differential Images
Recently, a powerful face detection method based on AdaBoost algorithm is drawing attention to various applications. This method provides face detection systems with a good detection rate, although a considerable number of weak classifiers are needed. This paper introduces weak classifiers which can not be or can be less influenced by gradual brightness changes in face regions or changes in lighting condition. Using a simple mathematical model for these changes, we have found that a second-order differentiation, e.g. Laplacian Operator, is very useful to cope with these changes. In order to show the effectiveness, we have compared the classification results for original and differential images with and without normalization. As a result, the second-order differentiation is found to be very effective, regardless of normalization of images. This result suggests the number of weak classifiers may be reduced to a great extent, while preserving equal detection capability.
Hongjin Zhu Shisong Zhu Toshio Koga
Graduate School of Science and Engineering Yamagata University Yonezawa-shi, Yamagata, Japan
国际会议
2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)
镇江
英文
718-722
2008-07-07（万方平台首次上网日期，不代表论文的发表时间）