Efficient locality-constrained occlusion coding for face recognition

• Published in: Neurocomputing (Amsterdam) Vol. 260; pp. 104 - 111
• Main Authors: Fu, Yuli, Wu, Xiaosi, Wen, Yandong, Xiang, Youjun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 18.10.2017
• Subjects: Efficiency; Face recognition; Locality-constrained Linear Coding; Occlusion estimating; Sparse Error Correction with Markov Random Fields; Occlusion estimating; Sparse Error Correction with Markov Random Fields; Face recognition; Efficiency; Locality-constrained Linear Coding
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2017.04.001

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 l1-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.