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Low rank group sparse representation based classifier for pose variation
S. Yadav, M. Singh, , , A. Majumdar
Published in IEEE Computer Society
Volume: 2016-August
Pages: 2986 - 2990
Face recognition under uncontrolled environment persists to be an unresolved problem having challenges such as varying pose, illumination, occlusion etc. In this research, we propose an algorithm for identification of faces with pose and illumination variations. An adaptive dictionary learning framework built upon group sparse representation classifier is presented in order to learn dictionary parameters and pose invariant sparse codes for given images. Low rank regularization is utilized for dictionary learning, to address the noise present in training samples that can hinder the discriminative power of the learnt dictionary. Experimental results illustrate state-of-the-art performance on the CMU Multi-PIE dataset. © 2016 IEEE.
About the journal
JournalData powered by TypesetProceedings - International Conference on Image Processing, ICIP
PublisherData powered by TypesetIEEE Computer Society