Gender Classification by Combining Facial and Hair Information

Authors:
Xiao-Chen Lian;Bao-Liang Lu
Affiliations:
Department of Computer Science and Engineering MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems, Shanghai Jiao Tong University, Shanghai, China 200240;Department of Computer Science and Engineering MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems, Shanghai Jiao Tong University, Shanghai, China 200240
Venue:
Advances in Neuro-Information Processing
Year:
2009

Citing 0
Cited 2

A new framework for small sample size face recognition based on weighted multiple decision templates

ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
A support vector machine classifier with automatic confidence and its application to gender classification

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Most of the existing gender classification approaches are based on face appearance only. In this paper, we present a gender classification system that integrates face and hair features. Instead of using the whole face we extract features from eyes, nose and mouth regions with Maximum Margin Criterion (MMC), and the hair feature is represented by a fragment-based encoding. We use Support Vector Machines with probabilistic output (SVM-PO) as individual classifiers. Fuzzy integration based classifier combination mechanism is used to fusing the four different classifiers on eyes, nose, mouth and hair respectively. The experimental results show that the MMC outperforms Principal Component Analysis and Fisher Discriminant Analysis and incorporating hair feature improves gender classification performance.