Undersampled face recognition via robust auxiliary dictionary learning

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Chia-Po Wei, Yu-Chiang Frank Wang

Abstract

In this paper, we address the problem of robust face recognition with undersampled training data. Given only one or few training images available per subject, we present a novel recognition approach, which not only handles test images with large intraclass variations such as illumination and expression. The proposed method is also to handle the corrupted ones due to occlusion or disguise, which is not present during training. This is achieved by the learning of a robust auxiliary dictionary from the subjects not of interest. Together with the undersampled training data, both intra and interclass variations can thus be successfully handled, while the unseen occlusions can be automatically disregarded for improved recognition. Our experiments on four face image datasets confirm the effectiveness and robustness of our approach, which is shown to outperform state-of-the-art sparse representation-based methods.

References

May 7, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Kuang-Chih LeeDavid J Kriegman
Nov 17, 2006·IEEE Transactions on Pattern Analysis and Machine Intelligence·Timo AhonenMatti Pietikäinen
Oct 12, 2007·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jie ZouGeorge Nagy
Dec 27, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·John WrightYi Ma
May 22, 2010·Proceedings of the ... International Conference on Automatic Face and Gesture Recognition·Ralph GrossSimon Baker
Mar 21, 2012·IEEE Transactions on Pattern Analysis and Machine Intelligence·Jiwen LuGang Wang
Jul 21, 2012·IEEE Transactions on Pattern Analysis and Machine Intelligence·Weihong DengJun Guo
Dec 28, 2012·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Meng YangDavid Zhang

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Citations

Apr 1, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Yuan GaoAlan L Yuille

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