Abstract
In recent years, sparse sampling techniques based on regression analysis have witnessed extensive applications in face recognition research. Presently, numerous sparse sampling models based on regression analysis have been explored by various researchers. Nevertheless, the recognition rates of the majority of these models would be significantly decreased when confronted with highly occluded and highly damaged face images. In this paper, a new wing-constrained sparse coding model(WCSC) and its weighted version(WWCSC) are introduced, so as to deal with the face recognition problem in complex circumstances, where the alternating direction method of multipliers (ADMM) algorithm is employed to solve the corresponding minimization problems. In addition, performances of the proposed method are examined based on the four well-known facial databases, namely the ORL facial database, the Yale facial database, the AR facial database and the FERET facial database. Also, compared to the other methods in the literatures, the WWCSC has a very high recognition rate even in complex situations where face images have high occlusion or high damage, which illustrates the robustness of the WWCSC method in facial recognition.
Abstract (translated)
近年来，基于回归分析的稀疏采样技术在人脸识别研究中得到了广泛应用。目前，许多基于回归分析的稀疏采样模型已经被各种研究人员探索和使用。然而，大多数这些模型在面对高度遮挡或严重损坏的人脸图像时识别率会显著下降。本文提出了一种新的受翼约束的稀疏编码模型（WCSC）及其加权版本（WWCSC），旨在解决复杂条件下的人脸识别问题，并利用交替方向乘子法（ADMM）算法来求解相应的最小化问题。此外，基于四个知名的面部数据库——ORL面部数据库、耶鲁面部数据库、AR面部数据库和FERET面部数据库，对所提出方法的性能进行了评估。与文献中的其他方法相比，在面部图像高度遮挡或损坏的情况下，WWCSC仍然具有非常高的识别率，这表明了WWCSC在人脸识别中具备强大的鲁棒性。
URL
https://arxiv.org/abs/2503.18652
