[1]DI Lan,JIAO Huiwen,LIANG Jiuzhen.Sparse comprehensive dictionary learning for small-sample face recognition[J].CAAI Transactions on Intelligent Systems,2021,16(2):218-227.[doi:10.11992/tis.201910028]
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Sparse comprehensive dictionary learning for small-sample face recognition

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 2 Page number: 218-227 Column: 学术论文—机器学习 Public date: 2021-03-05
Title:
Sparse comprehensive dictionary learning for small-sample face recognition
Author(s):
DI Lan1;  JIAO Huiwen1;  LIANG Jiuzhen3
1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China;
2. Laboratory of Ministry of Public Security for Road Traffic Safety, Wuxi 214151, China;
3. School of Information Science and Engineering, Changzhou University, Changzhou 213164, China
Keywords:
comprehensive dictionary learning; face recognition; class-specific dictionary learning; Fisher discrimination criterion; small sample; image expansion; mirror principle; extended interference dictionary; hybrid feature dictionary; low-rank dictionary
CLC:
TP394
DOI:
10.11992/tis.201910028
Abstract:
Traditional small-sample face recognition methods based on dictionary learning have disadvantages such as poor dictionary discrimination and lack of robustness. In this paper, we propose a sparse comprehensive dictionary learning model. This model effectively utilizes and generates facial changes, expands the diversity of training samples by the mirror principle and Fisher’s criterion, and extracts the commonalities, specialties, and anomalies between different categories of data by constructing a hybrid feature dictionary, extended interference dictionary, and low-rank dictionary atoms. This strategy improves the recognition rate of the algorithm and its ability to handle abnormal situations such as expression changes, pose changes, and occlusions. The results of simulation experiments performed on the face databases AR, YALEB, and LFW verify the effectiveness and feasibility of the proposed algorithm.
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