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Multimedia Tools and Applications 
February 2017, Volume 76, Issue 3, pp 4159–4177 | Cite as

Class specific centralized dictionary learning for face recognition



Sparse representation based classification (SRC) and collaborative representation based classification (CRC) have demonstrated impressive performance for visual recognition. SRC and CRC assume that the training samples in each class contribute equally to the dictionary and thus generate the dictionary that consists of the training samples in the corresponding class. This may lead to high residual error and instability, to the detriment of recognition performance. One solution is to use the class specific dictionary learning (CSDL) algorithm, which has greatly improved the classification accuracy. However, the CSDL algorithm fails to consider the constraints to sparse codes. In particular, it cannot guarantee that the sparse codes in the same class will be concentrated based on the learned dictionary for each class. Such concentration is actually beneficial to classification. To address these limitations, in this paper, we propose a class specific centralized dictionary learning (CSCDL) algorithm to simultaneously consider the desired characteristics for both dictionary and sparse codes. The blockwise coordinate descent algorithm and Lagrange multipliers are used to optimize the corresponding objective function. Extensive experimental results on face recognition benchmark datasets demonstrate the superior performance of our CSCDL algorithm compared with conventional approaches.


Centralized dictionary learning Face recognition Class specific



This paper is supported partly by the National Natural Science Foundation of China (Grant No. 61402535, No. 61271407), the Natural Science Foundation for Youths of Shandong Province, China (Grant No. ZR2014FQ001) , Qingdao Science and Technology Project (No. 14-2-4-111-jch), and the Fundamental Research Funds for the Central Universities, China University of Petroleum (East China) (Grant No. 14CX02169A).



© Springer Science+Business Media New York 2015




Liu, BD., Gui, L., Wang, Y. et al. Multimed Tools Appl (2017) 76: 4159. https://doi.org/10.1007/s11042-015-3042-2



