Extended JS']JSSL for Multi-Feature Face Recognition via Intra-Class Variant Dictionary

被引:0
作者
Lin, Guojun [1 ,2 ]
Zhang, Qinrui [2 ]
Zhou, Shunyong [1 ,2 ]
Jiang, Xingguo [2 ]
Wu, Hao [1 ,2 ]
You, Hairong [3 ]
Li, Zuxin [4 ]
He, Ping [2 ]
Li, Heng [5 ]
机构
[1] Sichuan Univ Sci & Engn, Artificial Intelligence Key Lab Sichuan Prov, Zigong 643000, Peoples R China
[2] Sichuan Univ Sci & Engn, Sch Automat & Informat Engn, Zigong 643000, Peoples R China
[3] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110819, Peoples R China
[4] Huzhou Univ, Sch Engn, Huzhou 313000, Peoples R China
[5] Hong Kong Polytech Univ, Smart Construct Lab BRE, Hong Kong, Peoples R China
基金
中国国家自然科学基金;
关键词
Face recognition; Feature extraction; Training; Testing; Dictionaries; Data mining; Collaboration; Sparse representation; image classification; multi-feature; face recognition; SPARSE REPRESENTATION; IMAGE; CLASSIFICATION; ROBUST; MODEL;
D O I
10.1109/ACCESS.2021.3089836
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper focuses on how to represent the testing face images for multi-feature face recognition. The choice of feature is critical for face recognition. The different features of the sample contribute differently to face recognition. The joint similar and specific learning (JSSL) has been effectively applied in multi-feature face recognition. In the JSSL, although the representation coefficient is divided into the similar coefficient and the specific coefficient, there is the disadvantage that the training images cannot represent the testing images well, because there are probable expressions, illuminations and disguises in the testing images. We think that the intra-class variations of one person can be linearly represented by those of other people. In order to solve well the disadvantage of JSSL, in the paper, we extend JSSL and propose the extended joint similar and specific learning (EJSSL) for multi-feature face recognition. EJSSL constructs the intra-class variant dictionary to represent the probable variation between the training images and the testing images. EJSSL uses the training images and the intra-class variant dictionary to effectively represent the testing images. The proposed EJSSL method is perfectly experimented on some available face databases, and its performance is superior to many current face recognition methods.
引用
收藏
页码:91807 / 91819
页数:13
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