Local Similarity based Linear Graph Embedding: A Robust Face Recognition Framework for SSPP problem
Authors: 
Fan Liu, Feng Xu, Ting Rui, Junhua ZhouAuthors Info & Claims
Fan Liu, Feng Xu, Ting Rui, Junhua ZhouAuthors Info & ClaimsPages 27 - 30
Abstract
As very popular methods for face recognition, subspace learning algorithms have attracted more and more attentions. However, they will suffer serious performance drop or fail to work when encountering SSPP problem. In this paper, we propose a robust framework called local similarity based linear graph embedding to solve this problem. Motivated by "divide and conquer" strategy, we first divide each face image into many local blocks and classify each block, and then integrate all the classification results by voting. To classify each block, we propose local similarity assumption, which not only makes LDA feasible to SSPP problem but also improves the performance of other subspace learning methods. Finally, we further summarize a general framework to unify these local similarity based subspace learning algorithms. Experimental results on two popular databases show that our methods not only generalize well to SSPP problem but also have strong robustness to expression, illumination, occlusion and time variation.
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Index Terms
- Local Similarity based Linear Graph Embedding: A Robust Face Recognition Framework for SSPP problem
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Published In
August 2016
360 pages
ISBN:9781450348508
DOI:10.1145/3007669
Copyright © 2016 ACM.
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- Xidian University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 August 2016
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- Short-paper
- Research
- Refereed limited
Funding Sources
- the Fundamental Research Funds for the Central Universities
- Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology)
- Jiangsu Planned Projects for Postdoctoral Research Funds
- National Natural Science Foundation of China
Conference
ICIMCS'16
ICIMCS'16: International Conference on Internet Multimedia Computing and Service
August 19 - 21, 2016
Xi'an, China
Acceptance Rates
ICIMCS'16 Paper Acceptance Rate 77 of 118 submissions, 65%;
Overall Acceptance Rate 163 of 456 submissions, 36%
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