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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
EditorsJ. Kittler, M. Petrou, M. Nixon
Pages446-449
Number of pages4
DOIs
Publication statusPublished - 2004 Dec 20
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: 2004 Aug 232004 Aug 26

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume4
ISSN (Print)1051-4651

Conference

ConferenceProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004
CountryUnited Kingdom
CityCambridge
Period04/8/2304/8/26

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

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