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Robust face recognition via sparse boosting representation
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DOI：10.1016/j.neucom.2016.06.071.png)
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摘要
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Recently linear representation provides an effective way for robust face recognition. However, the existing linear representation methods cannot make an adaptive adjustment in responding to the variations on facial image, so the generalization ability of these methods is limited. In this paper, we propose a sparse boosting representation classification (SBRC) for robust face recognition. To improve the effectiveness of representation coding, an error detection machine (EDM) with multiple error detectors (ED) in SBRC, is proposed to detect and remove destroyed features (i.e. pixels) on a testing image. SBRC has three advantages: First, it has good generalization ability, since the EDM can self-adjust the number of ED according to different variations; Second, EDM would boost the sparsity of coding vector; Third, its implementation is simple and efficient as the EDM is based on l(2) - norm. In addition, five popular face image databases including AR database, Extended Yale B database, ORL database, FERET database and LFW database were applied to validate the performance of SBRC. The superiority of SBRC is confirmed by comparing it with the state-of-the-art face recognition methods. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Sparse boosting
Linear representation
Face recognition
And error detection
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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The FERET database and evaluation procedure for face-recognition algorithms人脸识别算法的FERET数据库和评估程序
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分享收藏Noise-free representation based classification and face recognition experiments.png)
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