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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Vydáno v:2015 IEEE International Conference on Image Processing (ICIP) s. 291 - 295
Hlavní autoři: Zhong Zhao, Guocan Feng, Lifang Zhang, Jiehua Zhu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.09.2015
Témata:
Collaboration
Collaborative representation
Computational modeling
Encoding
Entropy
Face
face recognition
group structure
maximum entropy principle
Training
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Abstract 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.
AbstractList 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.
Author Lifang Zhang
Zhong Zhao
Guocan Feng
Jiehua Zhu
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  organization: Sch. of Math. & Comput. Sci., Sun Yat-sen Univ., Guangzhou, China
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  organization: Sch. of Math. & Comput. Sci., Sun Yat-sen Univ., Guangzhou, China
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  surname: Jiehua Zhu
  fullname: Jiehua Zhu
  organization: Dept. of Math. Sci., Georgia Southern Univ., Statesboro, GA, USA
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Snippet While sparse representation is heavily emphasized in many recent literatures, the importance of collaborative representation is usually ignored. In this paper,...
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SubjectTerms Collaboration
Collaborative representation
Computational modeling
Encoding
Entropy
Face
face recognition
group structure
maximum entropy principle
Training
Title Maximum entropy regularized group collaborative representation for face recognition
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