A method of gender classification by integrating facial, hairstyle, and clothing images

Kazuya Ueki; Hiromitsu Komatsu; Satoshi Imaizumi; Kenichi Kaneko; Satoshi Imaizumi; Nobuhiro Sekine; Jiro Katto; Tetsunori Kobayashi

doi:10.1109/ICPR.2004.1333798

A method of gender classification by integrating facial, hairstyle, and clothing images

Kazuya Ueki^*, Hiromitsu Komatsu, Satoshi Imaizumi, Kenichi Kaneko, Satoshi Imaizumi, Nobuhiro Sekine, Jiro Katto, Tetsunori Kobayashi

^*この研究の対応する著者

基幹理工学部

研究成果: Conference contribution

24 被引用数 (Scopus)

抄録

This paper presents a method of gender classification by integrating facial, hairstyle, and clothing images. Initially, input images are separated into facial, hairstyle and clothing regions, and independently learned PCAs and GMMs based on thousands of sample images are applied to each region. The classification results are then integrated into a single score using some known priors based on the Bayes rule. Experimental results showed that our integration strategy significantly reduced error rate in gender classification compared with the conventional facial only approach.

本文言語	English
ホスト出版物のタイトル	Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
編集者	J. Kittler, M. Petrou, M. Nixon
ページ	446-449
ページ数	4
DOI	https://doi.org/10.1109/ICPR.2004.1333798
出版ステータス	Published - 2004 12月 20
イベント	Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom 継続期間: 2004 8月 23 → 2004 8月 26

出版物シリーズ

名前	Proceedings - International Conference on Pattern Recognition
巻	4
ISSN（印刷版）	1051-4651

Conference

Conference	Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
国/地域	United Kingdom
City	Cambridge
Period	04/8/23 → 04/8/26

ASJC Scopus subject areas

コンピュータビジョンおよびパターン認識

文献へのアクセス

10.1109/ICPR.2004.1333798

他のファイルとリンク

引用スタイル

Ueki, K., Komatsu, H., Imaizumi, S., Kaneko, K., Imaizumi, S., Sekine, N., Katto, J., & Kobayashi, T. (2004). A method of gender classification by integrating facial, hairstyle, and clothing images. In J. Kittler, M. Petrou, & M. Nixon (Eds.), Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 (pp. 446-449). (Proceedings - International Conference on Pattern Recognition; Vol. 4). https://doi.org/10.1109/ICPR.2004.1333798

A method of gender classification by integrating facial, hairstyle, and clothing images. / Ueki, Kazuya; Komatsu, Hiromitsu; Imaizumi, Satoshi その他.
Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004. ed. / J. Kittler; M. Petrou; M. Nixon. 2004. p. 446-449 (Proceedings - International Conference on Pattern Recognition; Vol. 4).

研究成果: Conference contribution

Ueki, K, Komatsu, H, Imaizumi, S, Kaneko, K, Imaizumi, S, Sekine, N, Katto, J & Kobayashi, T 2004, A method of gender classification by integrating facial, hairstyle, and clothing images. in J Kittler, M Petrou & M Nixon (eds), Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004. Proceedings - International Conference on Pattern Recognition, vol. 4, pp. 446-449, Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, Cambridge, United Kingdom, 04/8/23. https://doi.org/10.1109/ICPR.2004.1333798

Ueki K, Komatsu H, Imaizumi S, Kaneko K, Imaizumi S, Sekine N その他. A method of gender classification by integrating facial, hairstyle, and clothing images. In Kittler J, Petrou M, Nixon M, editors, Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004. 2004. p. 446-449. (Proceedings - International Conference on Pattern Recognition). doi: 10.1109/ICPR.2004.1333798

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AB - This paper presents a method of gender classification by integrating facial, hairstyle, and clothing images. Initially, input images are separated into facial, hairstyle and clothing regions, and independently learned PCAs and GMMs based on thousands of sample images are applied to each region. The classification results are then integrated into a single score using some known priors based on the Bayes rule. Experimental results showed that our integration strategy significantly reduced error rate in gender classification compared with the conventional facial only approach.

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