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Efficient locality-constrained occlusion coding for face recognition
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PRE
AI
DOI：10.1016/j.neucom.2017.04.001.png)
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Abstract
En 中文
Occlusion is a common yet challenging problem in face recognition. Most of the existing approaches cannot achieve the accuracy of the recognition with high efficiency in the occlusion case. To address this problem, this paper proposes a novel algorithm, called efficient locality-constrained occlusion coding (ELOC), improving the previous sparse error correction with Markov random fields (SEC_MRF) algorithm. The proposed approach estimates and excludes occluded region by locality-constrained linear coding (LLC), which avoids the time-consuming 15-minimization and exhaustive subject-by-subject search during the occlusion estimation, and greatly reduces the running time of recognition. Moreover, by simplifying the regularization, the ELOC can be further accelerated. Experimental results on several face databases show that our algorithms significantly improve the previous algorithms in efficiency without losing too much accuracy. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Face recognition
Occlusion estimating
Locality-constrained Linear Coding
Sparse Error Correction with Markov
Random Fields
Efficiency
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
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