Learning discriminative subregions and pattern orders for facial gender classification

View/ Open
Embargoed until: 2021-09-01
File version
Accepted Manuscript (AM)
Author(s)
Chen, Zhong
Edwards, Andrea
Gao, Yongsheng
Zhang, Kun
Griffith University Author(s)
Year published
2019
Metadata
Show full item recordAbstract
Facial image-based gender classification has been widely used in many real-world applications. Most of the existing work, however, focuses on designing sophisticated or specific feature descriptors for the entire face, which neglects the discriminative information carried by facial components and pattern order combinations. To address the issue, in this paper, we first propose a generalized texture operator, i.e., the multi-spatial multi-order interlaced pattern (MMIP) matrix, to represent the gender information possessed by the facial subregions with textural pattern orders. A chain-type support vector machine (CSVM) based ...
View more >Facial image-based gender classification has been widely used in many real-world applications. Most of the existing work, however, focuses on designing sophisticated or specific feature descriptors for the entire face, which neglects the discriminative information carried by facial components and pattern order combinations. To address the issue, in this paper, we first propose a generalized texture operator, i.e., the multi-spatial multi-order interlaced pattern (MMIP) matrix, to represent the gender information possessed by the facial subregions with textural pattern orders. A chain-type support vector machine (CSVM) based feature vector selection scheme, is then developed to highlight the gender characteristics. As a result, the discriminative subregions and pattern orders are constructed as the feature representation for facial gender classification. We evaluate our proposed method on four benchmark datasets (i.e., FRGC 2.0, FERET, LFW and UND) for gender classification and demonstrate its interpretability, effectiveness and efficiency compared with state-of-the-art methods.
View less >
View more >Facial image-based gender classification has been widely used in many real-world applications. Most of the existing work, however, focuses on designing sophisticated or specific feature descriptors for the entire face, which neglects the discriminative information carried by facial components and pattern order combinations. To address the issue, in this paper, we first propose a generalized texture operator, i.e., the multi-spatial multi-order interlaced pattern (MMIP) matrix, to represent the gender information possessed by the facial subregions with textural pattern orders. A chain-type support vector machine (CSVM) based feature vector selection scheme, is then developed to highlight the gender characteristics. As a result, the discriminative subregions and pattern orders are constructed as the feature representation for facial gender classification. We evaluate our proposed method on four benchmark datasets (i.e., FRGC 2.0, FERET, LFW and UND) for gender classification and demonstrate its interpretability, effectiveness and efficiency compared with state-of-the-art methods.
View less >
Journal Title
Image and Vision Computing
Volume
89
Copyright Statement
© 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
Subject
Artificial Intelligence and Image Processing
Electrical and Electronic Engineering