Deep softmax collaborative representation for robust degraded face recognition
- Authors
- Vo, Duc My; Duc Manh Nguyen; Lee, Sang-Woong
- Issue Date
- Jan-2021
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- Softmax collaborative representation-based classification; Deep convolutional neural network; Adaptive ensemble of deep softmax collaborative representation-based classifiers; Mobile robots; Unconstrained face recognition; Deep learning; Image denoising
- Citation
- ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v.97
- Journal Title
- ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Volume
- 97
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/79724
- DOI
- 10.1016/j.engappai.2020.104052
- ISSN
- 0952-1976
- Abstract
- Deep convolutional neural networks (DCNN) have attracted much attention in the field of face recognition because they have achieved high performance than other approaches in the so-called in-the-wild datasets. However, in many real-world applications of face recognition, the performance of CNN-based algorithms is significantly decreased when images contain various kinds of degradations caused by random noise, motion blur, compression artifacts, uncontrolled illumination, and occlusion. Moreover, this is because the main weakness of existing DCNN models is the overfitting problem. To boost the recognition performance of stateof-the-art deep learning networks, we propose a deep softmax collaborative representation-based network, which can be used as a divide-and-conquer algorithm to help multiple DCCNs work together more effectively to solve multiple sub-problems of face reconstruction and classification. We demonstrated several experiments with challenging face recognition datasets. Our extensive experiments demonstrate that our proposed method is more robust and efficient in dealing with the challenging real-world problems in face recognition compared to related state-of-the-art methods.
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