Face gender classification: A statistical study when neutral and distorted faces are combined for training and testing purposes

作者：

Highlights：

• Study of gender recognition from neutral, expressive and occluded faces

• Comparison of global/local approaches, grey level/PCA/LBP features and three classifiers

• Three statistical tests over two performance measures are employed to support the conclusions.

• Local models surpass global ones with different types of training and test faces.

• Global and local models perform equally with the same type of training and test faces.

摘要

•Study of gender recognition from neutral, expressive and occluded faces•Comparison of global/local approaches, grey level/PCA/LBP features and three classifiers•Three statistical tests over two performance measures are employed to support the conclusions.•Local models surpass global ones with different types of training and test faces.•Global and local models perform equally with the same type of training and test faces.

论文关键词：Face analysis,Gender classification,Global/local representation,Cross-database experiment

论文评审过程：Received 21 March 2013, Revised 4 October 2013, Accepted 8 November 2013, Available online 16 November 2013.

论文官网地址：https://doi.org/10.1016/j.imavis.2013.11.001