IEEE Conference on Computer Vision and Pattern Recognition. 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
In Defense of Sparsity Based Face Recognition
作者:
Deng， W.， Hu， J. and Guo， J.
关键词:
Training； Dictionaries； Face recognition； Prototypes； Accuracy； Image recognition； Databases
摘要:
The success of sparse representation based classification (SRC) has largely boosted the research of sparsity based face recognition in recent years. A prevailing view is that the sparsity based face recognition performs well only when the training images have been carefully controlled and the number of samples per class is sufficiently large. This paper challenges the prevailing view by proposing a ``prototype plus variation'' representation model for sparsity based face recognition. Based on the new model, a Superposed SRC (SSRC), in which the dictionary is assembled by the class centroids and the sample-to-centroid differences, leads to a substantial improvement on SRC. The experiments results on AR, FERET and FRGC databases validate that, if the proposed prototype plus variation representation model is applied, sparse coding plays a crucial role in face recognition, and performs well even when the dictionary bases are collected under uncontrolled conditions and only a single sample per classes is available.
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