A real-time classification model based on joint sparse-collaborative representation

• Published in: Journal of real-time image processing Vol. 18; no. 5; pp. 1837 - 1849
• Main Authors: Li, Yanting, Jin, Junwei, Chen, C. L. Philip
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2021; Springer Nature B.V
• Subjects: Classification; Collaboration; Computer Graphics; Computer Science; Exact solutions; Face recognition; Image Processing and Computer Vision; Multimedia Information Systems; Pattern Recognition; Real time; Representations; Signal,Image and Speech Processing; Special Issue Paper; Training; Sparse representation; Collaborative representation; Real-time face recognition; Two-phase test sample representation; Pattern recognition
• ISSN: 1861-8200; 1861-8219
• DOI: 10.1007/s11554-021-01167-y

Due to the low computational complexity, the two-phase test sample representation method shows outstanding advantages in the field of real-time face recognition. However, its first phase does not fully consider the imbalance of the determined K -nearest training samples. This defect is not well reflected and will seriously affect the recognition accuracy of the second phase. In this paper, we explore the above issues and propose to incorporate the unselected training samples into the modeling process. The proposed method not only allows the unselected samples to perform the final representation but also makes the selected nearest training samples play a more significant role than the unselected ones in classification. It is efficiently solved with an analytical solution. The rationales and probability interpretation of the proposed method are presented to further guarantee its rationality and effectiveness. Extensive experiments on diverse face databases are conducted to demonstrate the superior recognition performance in comparison with other competing classifiers.