发布日期: 2025-11-16

更新日期: 2025-11-17

文章字数: 835

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2025-11-16 更新

Hybrid second-order gradient histogram based global low-rank sparse regression for robust face recognition

Authors:Hongxia Li, Ying Ji, Yongxin Dong, Yuehua Feng

Low-rank sparse regression models have been widely applied in the field of face recognition. To further address the challenges caused by complex occlusions and illumination variations, this paper proposes a Hybrid Second-Order Gradient Histogram based Global Low-Rank Sparse Regression (H2H-GLRSR) model. Specifically, a novel feature descriptor called the Hybrid Second-Order Gradient Histogram (H2H) is first designed to more effectively characterize the local structural features of facial images. Then, this descriptor is integrated with the Sparse Regularized Nuclear Norm based Matrix Regression (SR$_$NMR). Moreover, a global low-rank constraint is imposed on the residual matrix, enabling the model to better capture the global correlations inherent in structured noise. Experimental results demonstrate that the proposed method significantly outperforms existing regression-based classification approaches under challenging scenarios involving occlusions, illumination changes, and unconstrained environments.

低秩稀疏回归模型已在人脸识别领域得到广泛应用。为了解决复杂遮挡和光照变化带来的挑战，本文提出了一种基于混合二阶梯度直方图的全局低秩稀疏回归（H2H-GLRSR）模型。具体来说，首先设计了一种名为混合二阶梯度直方图（H2H）的新型特征描述符，以更有效地表征面部图像局部结构特征。然后，将该描述符与基于稀疏正则化核范数矩阵回归（SR_NMR）相结合。此外，对残差矩阵施加全局低秩约束，使模型能够更好地捕捉结构化噪声中的全局相关性。实验结果表明，在涉及遮挡、光照变化和无约束环境的挑战场景中，该方法显著优于现有的基于回归的分类方法。

论文及项目相关链接

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Summary

该论文针对人脸识别中的复杂遮挡和光照变化问题，提出了一种基于二阶混合梯度直方图的全局低秩稀疏回归模型（H2H-GLRSR）。通过设计新型特征描述器Hybrid Second-Order Gradient Histogram（H2H），更有效地表征面部图像局部结构特征，并将其与稀疏正则化核范数矩阵回归相结合。同时，对残差矩阵施加全局低秩约束，以更好地捕捉结构化噪声中的全局相关性。实验结果显示，该方法在遮挡、光照变化和无约束环境下的回归分类方法表现优越。

Key Takeaways