A hybrid-supervision learning algorithm for real-time un-completed face recognition. (July 2022)

• Record Type: Journal Article
• Authors: Zhao, Shuhuan; Liu, Wen; Liu, Shuaiqi; Ge, Jiaqi; Liang, Xiaolin
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: Face recognition -- Feature fusion -- Hybrid supervised learning -- Multiple marginal Fisher analysis; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108090
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: The main objective of this paper is to improve the performance of un-completed face recognition by hybrid-supervised method. HMMFA is proposed, which improved the inter-class separability and the efficiency of feature extraction. Image partition is performed before PCANet to enhance its robustness for occlusion. Abstract: It is still an important and challenging problem for face recognition with occlusion, small sample size, various expressions, and poses, called un-completed face recognition. So we design a simple but effective hybrid-supervision learning frame by fusing the advantages of supervised and unsupervised features. In the supervised branch, we propose an effective feature learning method: HMMFA. In the unsupervised branch, we improve the PCANet to extract more effective local information. In the fusion stage, we further extract the discriminant features contained in the hybrid features and then take SVM as the final classifier. Because the proposed method requires no auxiliary set and has less parameter number than that of deep learning methods, it has a low storage requirement, which makes it more economical and practical for small communities. Experiments on four databases show the effectiveness and efficiency of our method. Graphical abstract: Image, graphical abstract