Gender dictionary learning for gender classification

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• 作者：Hossein Moeini ; ^{hosseinmoeini@semnan.ac.ir} ; ^{h.moeini.2014@ieee.org} ; ^{hosseinmoeini@aol.com} ; Saeed Mozaffari
• 关键词：Gender classification ; Real-world face images ; Dictionary learning ; Probability decision making ; Sparse representation
• 刊名：Journal of Visual Communication and Image Representation
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：42
• 期：Complete
• 页码：1-13
• 全文大小：2547 K
• 卷排序：42

文摘

A sparse dictionary learning approach is proposed for purpose of gender classification. Two separate dictionaries are proposed for male and female genders alongside with feature dictionary. Two dictionary learning methods are proposed to learn concurrent three dictionaries. A probability decision making approach is proposed to infer gender from their male and female probabilities. It improves the gender classification rate on the FERET, LFW and Groups databases.