Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (13): 147-153.DOI: 10.3778/j.issn.1002-8331.2004-0001

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Face Recognition via Discriminative Non-negative Representation Based Classification

XU Ranran, WU Xiaojun, YIN Hefeng   

  1. School of IoT Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2021-07-01 Published:2021-06-29



  1. 江南大学 物联网工程学院,江苏 无锡 214122


The non-negative representation-based classifier has achieved outstanding performance among face recognition algorithms, however, the correlation of the representations between different categories is detrimental to the classification. It ignores the structural information between different categories. In order to solve this problem, the paper proposes a face recognition algorithm based on Discriminative Non-negative Representation. Firstly, on the basis of non-negative constraints, regularization term is introduced to reduce the correlation of the representations between different categories. Then, the variables are optimized by the Alternating Direction Method of Multipliers(ADMM). Finally, the test sample is classified into the class that leads to the least reconstruction error. The experimental results on four benchmark datasets show that the proposed classification algorithm is superior to compared approaches in terms of recognition accuracy.

Key words: sparse representation, non-negative representation, discriminative information, face recognition



关键词: 稀疏表示, 非负表示, 鉴别信息, 人脸识别