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Dual sparse learning via data augmentation for robust facial image classification
Zeng,Shaoning1,2; Zhang,Bob1; Zhang,Yanghao3; Gou,Jianping4
Source PublicationInternational Journal of Machine Learning and Cybernetics

Data augmentation has been utilized to improve the accuracy and robustness of face recognition algorithms. However, most of the previous studies focused on using the augmentation techniques to enlarge the feature set, while the diversity produced by the virtual samples lacked sufficient attention. In sparse dictionary learning-based face recognition, l-based sparse representation (SR) and SVD-based dictionary learning (DL) both have shown promising performance. How to utilize both of them in an enhanced training process by data augmentation is still unclear. This paper proposes a novel method that utilizes the sample diversity generated by data augmentation and integrates sparse representation with dictionary learning, to learn dual sparse features for robust face recognition. An additional feature set is created by applying sample augmentation via simply horizontal flipping of face images. The two sparse models, l-based SR and SVD-based DL, are integrated together using our new proposed objective function. Under two-level fusion of both data and classifiers, the diversity between two training sets is well learned and utilized, in three implementations, to obtain a robust face recognition. After conducting extensive experiments on some popular facial datasets, we demonstrate the proposed method can produce a higher classification accuracy than many state-of-the-art algorithms, and it can be considered as a promising option for image-based face recognition. Our code is released at GitHub.

KeywordDictionary Learning Image Classification L1 Regularization Sparse Representation
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000515656300001
Scopus ID2-s2.0-85078440656
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Cited Times [WOS]:6   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Corresponding AuthorZhang,Bob
Affiliation1.Pattern Analysis and Machine Intelligence Group,Department of Computer and Information Science,University of Macau,Macao
2.School of Computer Science and Engineering,Huizhou University,Huizhou,China
3.Electronics and Computer Science,University of Southampton,Southampton,SO17 1BJ,United Kingdom
4.College of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang,China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zeng,Shaoning,Zhang,Bob,Zhang,Yanghao,et al. Dual sparse learning via data augmentation for robust facial image classification[J]. International Journal of Machine Learning and Cybernetics,2020,11(8):1717-1734.
APA Zeng,Shaoning,Zhang,Bob,Zhang,Yanghao,&Gou,Jianping.(2020).Dual sparse learning via data augmentation for robust facial image classification.International Journal of Machine Learning and Cybernetics,11(8),1717-1734.
MLA Zeng,Shaoning,et al."Dual sparse learning via data augmentation for robust facial image classification".International Journal of Machine Learning and Cybernetics 11.8(2020):1717-1734.
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