Fuzzy Linear Regression Discriminant Projection for Face Recognition

How to capture distinctive features from facial images when there are large variations in illumination, poses, and expressions is important for the face recognition problems. This paper introduces a novel algorithm called fuzzy linear regression discriminant projection (FLRDP) for face recognition....

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Bibliographic Details
Published inIEEE access Vol. 5; pp. 4340 - 4349
Main Authors Huang, Pu, Gao, Guangwei, Qian, Chengshan, Yang, Geng, Yang, Zhangjing
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Face recognition
Feature extraction
fuzzy set
Image reconstruction
LRC
Manifolds
membership degree
Object recognition
Principal component analysis
Regression analysis
Testing
Training
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Summary:How to capture distinctive features from facial images when there are large variations in illumination, poses, and expressions is important for the face recognition problems. This paper introduces a novel algorithm called fuzzy linear regression discriminant projection (FLRDP) for face recognition. The proposed algorithm FLRDP seeks to generate an efficient subspace for the LRC method and could effectively handle variations between facial images. To be specific, FLRDP first computes the gradual membership degrees of each sample to corresponding classes, and then incorporates such membership degree information into the construction of the fuzzy between-class and within-class reconstruction errors. Finally, the criterion function is derived via maximizing the ratio of the fuzzy between-class reconstruction error to the fuzzy within-class reconstruction error. Experimental results carried out on the ORL, CMU PIE, and FERET face databases show the superiority of our proposed method over other state-of-the-art algorithms.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2680437