Learning discriminative subregions and pattern orders for facial gender classification

作者：

Highlights：

• We propose a generic multi-spatial multi-order feature descriptor.

• We develop a chain-type SVM based feature vector selection method.

• We study the roles of discriminative facial subregions and pattern orders for facial gender classification.

• Empirical studies on four widely used datasets demonstrate the efficacy of the proposed method.

摘要

•We propose a generic multi-spatial multi-order feature descriptor.•We develop a chain-type SVM based feature vector selection method.•We study the roles of discriminative facial subregions and pattern orders for facial gender classification.•Empirical studies on four widely used datasets demonstrate the efficacy of the proposed method.

论文关键词：Discriminative subregions,Pattern order,Chain-type SVM,Feature vector selection,Facial gender classification

论文评审过程：Received 7 February 2018, Revised 30 November 2018, Accepted 27 June 2019, Available online 15 July 2019, Version of Record 31 July 2019.

论文官网地址：https://doi.org/10.1016/j.imavis.2019.06.012