Gender Classification of Complex Face Images Based on AdaBoost

DOI

10.2991/icmmcce-17.2017.240How to use a DOI?

Keywords

machine learning; gender classification; Adaboost; feature extraction

Abstract

This paper proposes a face-based gender classifier which is based on Adaboost, and selects the face database with different color, angle and illumination. A variety of feature extraction methods are used to reduce the dimension, noise and calculation of the sample, and ensure a high recognition rate. The simulation results show that the proposed classifier can complete the gender classification work with the interference of skin color, angle and illumination, and the error rate is only 7.5%, what is more, the training and recognition speed is also get improved.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Junrui Wang
AU  - Qichuan Tian
AU  - Manli Wang
AU  - Xiaohui Wu
PY  - 2017/09
DA  - 2017/09
TI  - Gender Classification of Complex Face Images Based on AdaBoost
BT  - Proceedings of the 2017 5th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2017)
PB  - Atlantis Press
SP  - 1365
EP  - 1370
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmcce-17.2017.240
DO  - 10.2991/icmmcce-17.2017.240
ID  - Wang2017/09
ER  -