Maximum Entropy Regularized Group Collaborative Representation for Face Recognition
Proceedings of IEEE International Conference on Imaging Processing
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.
Zhao, Zhong, Guocan Feng, Lifang Zhang, Jiehua Zhu.
"Maximum Entropy Regularized Group Collaborative Representation for Face Recognition."
Proceedings of IEEE International Conference on Imaging Processing: 291-295 Quebec City, Canada: IEEE.
doi: 10.1109/ICIP.2015.7350806 isbn: 978-1-4799-8339-1