REAL-TIME GENDER RECOGNITION FOR UNCONTROLLED ENVIRONMENT OF REAL-LIFE IMAGES

Duan-Yu Chen, Kuan-Yi Lin

2010

Abstract

Gender recognition is a challenging task in real life images and surveillance videos due to their relatively low-resolution, under uncontrolled environment and variant viewing angles of human subject. Therefore, in this paper, a system of real-time gender recognition for real life images is proposed. The contribution of this work is fourfold. A skin-color filter is first developed to filter out non-face noises. In order to make the system robust, a mechanism of decision making based on the combination of surrounding face detection, context-regions enhancement and confidence-based weighting assignment is designed. Experimental results obtained by using extensive dataset show that our system is effective and efficient in recognizing genders for uncontrolled environment of real life images.

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Paper Citation


in Harvard Style

Chen D. and Lin K. (2010). REAL-TIME GENDER RECOGNITION FOR UNCONTROLLED ENVIRONMENT OF REAL-LIFE IMAGES . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 357-362. DOI: 10.5220/0002823203570362


in Bibtex Style

@conference{visapp10,
author={Duan-Yu Chen and Kuan-Yi Lin},
title={REAL-TIME GENDER RECOGNITION FOR UNCONTROLLED ENVIRONMENT OF REAL-LIFE IMAGES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={357-362},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002823203570362},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - REAL-TIME GENDER RECOGNITION FOR UNCONTROLLED ENVIRONMENT OF REAL-LIFE IMAGES
SN - 978-989-674-029-0
AU - Chen D.
AU - Lin K.
PY - 2010
SP - 357
EP - 362
DO - 10.5220/0002823203570362