Comparing studies of learning methods for human face gender recognition

Authors:
Yanbin Jiao;Jucheng Yang;Zhijun Fang;Shanjuan Xie;Dongsun Park
Affiliations:
School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China;College of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin, China;School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China;Division of Electronics and Information Engineering, Chonbuk National University, Korea;Division of Electronics and Information Engineering, Chonbuk National University, Korea
Venue:
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Year:
2012

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Abstract

Recently, some machine learning algorithms such as Back Propagation (BP) neural network, Support Vector Machine (SVM) and other algorithms are proposed and proven to be useful for human face gender recognition. However, they have lots of shortcomings, such as, requiring setting a large number of training parameters, difficultly choosing the appropriate parameters, and much time consuming for training. In this paper, we proposes a new learning method to use Extreme Learning Machine (ELM) for face gender recognition and compare it with other two main state-of-the-art learning methods for face gender recognition by using BP, SVM respectively. Experimental results on public databases show that ELM plays the best performances for human face gender recognition with higher recognition rate and faster speed. Compared with SVM, the learning speed of ELM is obvious reduced. And compared with BP neural network, it has faster speed, higher precision, and better generalization ability.