Regularized Robust Coding for Face Recognition

• Published in: IEEE transactions on image processing Vol. 22; no. 5; pp. 1753 - 1766
• Main Authors: Meng Yang, Lei Zhang, Jian Yang, Zhang, D.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2013; Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Applied sciences; Biometric Identification - methods; Coding, codes; Databases, Factual; Encoding; Exact sciences and technology; Face; Face - anatomy & histology; Face recognition; Facial Expression; Female; Humans; Image coding; Image processing; Image Processing, Computer-Assisted - methods; Information, signal and communications theory; Laplace equations; Lighting; Male; Minimization; Pattern recognition; regularization; robust coding; Robustness; Signal and communications theory; Signal processing; sparse representation; Telecommunications and information theory; Training; Biometrics; State of the art; Face recognition; Image processing; L1 approximation; regularization; Gaussian distribution; Iterative method; L2 approximation; Pattern recognition; Algorithm; Computational complexity; Laplacian; Learning; Linear combination; Coding; Residual impurity; A posteriori estimation; Database; Sparse representation; Automatic recognition; robust coding
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2012.2235849

Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l 2 -norm or l 1 -norm of the coding residual. Such a sparse coding model assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be effective enough to describe the coding residual in practical FR systems. Meanwhile, the sparsity constraint on the coding coefficients makes the computational cost of SRC very high. In this paper, we propose a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients. By assuming that the coding residual and the coding coefficient are respectively independent and identically distributed, the RRC seeks for a maximum a posterior solution of the coding problem. An iteratively reweighted regularized robust coding ( IR 3 C ) algorithm is proposed to solve the RRC model efficiently. Extensive experiments on representative face databases demonstrate that the RRC is much more effective and efficient than state-of-the-art sparse representation based methods in dealing with face occlusion, corruption, lighting, and expression changes, etc.