[1]QIAN Jianbin,CHEN Xiuhong.Self-adaptive multi-phase linear reconstruction representation based classification for face recognition[J].CAAI Transactions on Intelligent Systems,2020,15(5):964-971.[doi:10.11992/tis.201904002]
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Self-adaptive multi-phase linear reconstruction representation based classification for face recognition

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 5 Page number: 964-971 Column: 学术论文—机器感知与模式识别 Public date: 2020-09-05
Title:
Self-adaptive multi-phase linear reconstruction representation based classification for face recognition
Author(s):
QIAN Jianbin;  CHEN Xiuhong
School of Digital Media, Jiangnan University, Wuxi 214122, China
Keywords:
face recognition; self-adaptive; multi-phase; linear reconstruction; representation coefficient; classification method; sparse representation; collaborative representation; pattern recognition
CLC:
TP391.4
DOI:
10.11992/tis.201904002
Abstract:
Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the linear reconstruction measure can be well used for classifying objects. But RBC methods performs very poorly on large-class-databases and in order to solve the problem of poor performance, a self-adaptive multi-phase linear reconstruction representation based classification (MPRBC) method is proposed. In this process, at first, the reconstruction coefficients regularized by L1-norm or L2-norm are obtained. Then the similar classes are selected according to the sum of the representation coefficients in each class, and all samples of similar classes are retained as training samples for the next stage. This strategy finally produces a sparse class probability distribution with higher classification confidence. The similar classes are selected adaptively according to the values of class coefficients, which improves the efficiency of the classification. Experimental results show that the proposed method is better than other RBC methods, especially on large-class-databases, and CPU time remains relatively low.
References:
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