Maximum entropy regularized group collaborative representation for face recognition

While sparse representation is heavily emphasized in many recent literatures, the importance of collaborative representation is usually ignored. In this paper, we exploit the advantage of collaborative representation and propose a maximum entropy regularized group collaborative representation (MECR)...

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Bibliographic Details
Published in:2015 IEEE International Conference on Image Processing (ICIP) pp. 291 - 295
Main Authors: Zhong Zhao, Guocan Feng, Lifang Zhang, Jiehua Zhu
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2015
Subjects:
Collaboration
Collaborative representation
Computational modeling
Encoding
Entropy
Face
face recognition
group structure
maximum entropy principle
Training
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Summary:While sparse representation is heavily emphasized in many recent literatures, the importance of collaborative representation is usually ignored. In this paper, we exploit the advantage of collaborative representation and propose a maximum entropy regularized group collaborative representation (MECR) algorithm for face recognition. MECR takes the group structure of the face data into consideration under the framework of collaborative representation, and uses maximum entropy principle to obtain discriminative coding for classification. Experiments show that MECR outperforms several state-of-the-art coding methods and dictionary learning methods on some benchmark face databases.
DOI:10.1109/ICIP.2015.7350806