Multi-view gender classification using hierarchical classifiers structure

Authors:
Tian-Xiang Wu;Bao-Liang Lu
Affiliations:
Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Center for Brain-Like Computing and Machine Intelligence, Dept. of Computer Science and Eng., Shanghai Jiao Tong Univ., Shanghai, China and MOE-Microsoft Key Lab. for Int. Computing and Int. Syste ...
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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Abstract

In this paper, we propose a hierarchical classifier structure for gender classification based on facial images by reducing the complexity of the original problem. In the proposed framework, we first train a classifier, which will properly divide the input images into several groups. For each group, we train a gender classifier, which is called expert. These experts can be any commonly used classifiers, such as Support Vector Machine (SVM) and neural network. The symmetrical characteristic of human face is utilized to further reduce the complexity. Moreover, we adopt soft assignment instead of hard one when dividing the input data, which can reduce the error introduced by the division. Experimental results demonstrate that our framework significantly improves the performance.