Regularized Robust Coding for Face Recognition (1202.4207v2)

Abstract: 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 l2-norm or l1-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 SRC's computational cost 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 (IR3C) 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.

• The paper introduces Regularized Robust Coding (RRC), which leverages iterative reweighting of residuals to improve face recognition performance under occlusions and noise.
• It presents two model variants, RRC_L1 and RRC_L2, that balance outlier suppression with computational efficiency, outperforming traditional SRC methods.
• Extensive experiments on benchmark databases like Extended Yale B and AR validate the method’s robustness and practical applicability in real-world scenarios.

Review: Regularized Robust Coding for Face Recognition

The paper "Regularized Robust Coding for Face Recognition" by Meng Yang et al. presents a novel approach to face recognition, addressing the limitations of sparse representation-based classification (SRC) methods in handling various occlusions and corruptions. The proposed method, named Regularized Robust Coding (RRC), aims to offer an efficient and reliable solution by employing regularized regression coefficients and assuming independent and identically distributed coding residuals and coefficients.

The core innovation of RRC lies in its departure from the traditional sparse coding models that rely heavily on the computationally expensive $\ell_1$-minimization, which may not always effectively model the non-Gaussian noise present in face recognition tasks. By focusing on a maximum a posteriori (MAP) estimation, the RRC method provides a robust alternative that seeks to enhance both efficiency and efficacy when coding a given signal.

Methodology

The RRC model is structured to improve robustness against noise and occlusions by iteratively reweighting the residuals and coefficients in the coding process. The coding problem is addressed by transforming it into a form that leverages reweighted least squares, allowing the model to flexibly adapt to the distribution of pixel values in the face images. Two key variants, RRC_L1 and RRC_L2, are explored, offering options for $\ell_1$-norm and $\ell_2$-norm regularization, respectively.

Experimental Evaluation

The authors conducted extensive experiments on various benchmark face databases, such as Extended Yale B and AR, to demonstrate the effectiveness of the RRC method. The results show that RRC consistently outperforms state-of-the-art techniques like SRC, locality-constrained linear coding (LLC), and correntropy-based sparse representation (CESR), particularly in scenarios involving high levels of occlusion and corruption.

Notably, RRC_L1 and RRC_L2 showed promising results across different tasks, with RRC_L1 generally providing higher accuracy due to its enhanced outlier suppression capabilities. The RRC models demonstrated strong robustness to occlusions, often surpassing earlier methods like GSRC in both accuracy and computational cost. Furthermore, the experiments on real-world occlusions, such as disguises in the AR database, reinforced the strength of the RRC approach in practical applications.

Computational Efficiency

The paper also highlights the computational efficiency of the RRC model, especially when using $\ell_2$-norm regularization. Compared to SRC, which typically employs computationally intensive $\ell_1$-minimization, RRC_L2 offers a significant reduction in computational complexity without compromising performance, making it suitable for real-time applications in face recognition systems.

Implications and Future Directions

The advances presented in this paper have both practical and theoretical implications for the field of AI. The introduction of a robust coding model that effectively deals with facial variations and is computationally feasible opens up new possibilities for deploying face recognition systems in less controlled environments. Future applications could leverage the adaptive nature of RRC to further enhance performance in dynamic and unpredictable settings.

In addition, the methodology proposed invites further exploration into the interplay between different norms and coding frameworks, potentially leading to even more effective solutions for pattern recognition tasks. Future research may build on the RRC framework to address other challenges in computer vision and beyond, such as object recognition under adverse conditions.

In conclusion, "Regularized Robust Coding for Face Recognition" provides a compelling and comprehensive contribution to the domain by addressing critical shortcomings in existing methodologies and offering a robust, efficient alternative suitable for a variety of challenging real-world conditions.