Abstract When the face recognition method based on regression analysis is applied to the incomplete matrix, it completes the matrix firstly before using the face recognition method. Thus, the classification performance is reduced. To solve the problem, a face recognition method based on low-rank representation and low-rank matrix completion is proposed by integrating low-rank matrix completion and low-rank representation learning into a single model. The low-rank representation coefficient matrix is computed alternately and the missing entries are recovered by minimizing the representation coefficients and matrix rank. Then, the nearest neighbor classifier is used to classify the samples. Experimental results on several open face datasets show that the proposed method effectively improves the recognition performance and reduces the error of matrix completion while the entries of the training sample matrix are randomly missing.
Fund:Supported by National Natural Science Foundation of China(No.11571074,71273053), Natural Science Foundation of Fujian Province(No.2018J01666)
About author:: (WANG Binfu, master student. His research interests include data mining and pa-ttern recognition.) (CHEN Xiaoyun(Corresponding author), Ph.D., professor. Her research interests include data mining, pattern recognition and machine learning.) (XIAO Bingsen, master student. His research interests include data mining and pa-ttern recognition.)
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