Laplacian-Uniform Mixture-Driven Iterative Robust Coding With Applications to Face Recognition Against Dense Errors

Outliers due to occlusion, pixel corruption, and so on pose serious challenges to face recognition despite the recent progress brought by sparse representation. In this article, we show that robust statistics implemented by the state-of-the-art methods are insufficient for robustness against dense g...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 31; H. 9; S. 3620 - 3633
Hauptverfasser: Zheng, Huicheng, Lin, Dajun, Lian, Lina, Dong, Jiayu, Zhang, Peipei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Approximation algorithms
Coding
Coding residual
Corruption
Encoding
Error correction
Error correction & detection
Error detection
Face
Face recognition
Facial recognition technology
Iterative methods
Minimization
non-convexity
Objective function
Occlusion
outlier
Outliers (statistics)
Pattern recognition
Representations
Robustness
sparse representation
Training
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:Outliers due to occlusion, pixel corruption, and so on pose serious challenges to face recognition despite the recent progress brought by sparse representation. In this article, we show that robust statistics implemented by the state-of-the-art methods are insufficient for robustness against dense gross errors. By modeling the distribution of coding residuals with a Laplacian-uniform mixture, we obtain a sparse representation that is significantly more robust than the previous methods. The nonconvex error term of the implemented objective function is nondifferentiable at zero and cannot be properly addressed by the usual iteratively reweighted least-squares formulation. We show that an iterative robust coding algorithm can be derived by local linear approximation of the nonconvex error term, which is both effective and efficient. With iteratively reweighted <inline-formula> <tex-math notation="LaTeX">l_{1} </tex-math></inline-formula> minimization of the error term, the proposed algorithm is capable of handling the sparsity assumption of the coding errors more appropriately than the previous methods. Notably, it has the distinct property of addressing error detection and error correction cooperatively in the robust coding process. The proposed method demonstrates significantly improved robustness for face recognition against dense gross errors, either contiguous or discontiguous, as verified by extensive experiments.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2019.2945372