Fusion of multiple facial regions for expression-invariant gender classification

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概要

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• A novel gender classification method is presented which fuses information acquired from multiple facial regions for improving overall performance. It is able to compensate for facial expression even when training samples contain only neutral expression. We perform experimental investigation to evaluate the significance of different facial regions in the task of gender classification. Three most significant regions are used in our fusion-based method. The classification is performed by using support vector machines based on the features extracted using two-dimension principal component analysis. Experiments show that our fusion-based method is able to compensate for facial expressions and obtained the highest correct classification rate of 95.33%.

• The Institute of Electronics, Information and Communication Engineersの論文

著者

• Lu Li

  Institute Of Image Processing And Pattern Recognition Shanghai Jiao Tong University

• Shi Pengfei

  Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University

• Shi Pengfei

  Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University

• Lu Li

  Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University

関連論文

• Fusion of Multiple Facial Features for Age Estimation
• Sexual Dimorphism Analysis and Gender Classification in 3D Human Face
• 3D Face Landmarking Method under Pose and Expression Variations
• Fusion of multiple facial regions for expression-invariant gender classification
• Efficient iris recognition system based on iris anatomical structure

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