A method for detecting human face region based on generation and selection of kernel features

Junya Arakawa, Kenichi Morooka, Yousun Kang, Hiroshi Nagahashi

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抜粋

Recent researches for detecting face regions from images have paid attention to high dimensional kernel features(KFs), which are obtained by a non-linear transformation of original features extracted from images. A Support Vector Machine(SVM) is one of the most prominent learning algorithms for KFs. However, SVM is time-consuming because of needing a large number of KFs to improve the accuracy of the classification. This paper proposes a new method that constructs a classifier between face and non-face regions by generating and choosing KFs based on Kullback-Leibler Divergence(KLD). The KLD means a distance between two distributions of face and non-face data under a given KF, and some KFs of large KLDs are selected for the face detection. Moreover, the use of KLD enables us to generate new KFs, and to deal with different kinds of KFs concurrently. Some experiments show that our method can reduce the number of KFs much more than SVM, and achieve almost equal or better detection rate than that of SVM.

元の言語英語
ホスト出版物のタイトル2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
ページ2191-2196
ページ数6
3
DOI
出版物ステータス出版済み - 2004
外部発表Yes
イベント2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004 - The Hague, オランダ
継続期間: 10 10 200410 13 2004

その他

その他2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004
オランダ
The Hague
期間10/10/0410/13/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • これを引用

    Arakawa, J., Morooka, K., Kang, Y., & Nagahashi, H. (2004). A method for detecting human face region based on generation and selection of kernel features. : 2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004 (巻 3, pp. 2191-2196) https://doi.org/10.1109/ICSMC.2004.1400653