提出新型鲁棒人脸识别模型，应对遮挡和损伤图像
Robust face recognition based on the wing loss and the $\ell_1$ regularization
- 引入翼形约束稀疏编码与l1正则化，提升模型鲁棒性
- 在四大数据集上实现高识别率，尤其在严重遮挡时表现优异
- 适合处理真实场景中受干扰的人脸识别任务
近年来，基于回归分析的稀疏采样技术在人脸识别领域得到广泛应用。然而，多数现有模型在面对高度遮挡或严重损坏的人脸图像时，识别率显著下降。本文提出一种新的翼形约束稀疏编码模型（WCSC）及其加权版本（WWCSC），以应对复杂环境下的人脸识别问题，并采用交替方向乘子法（ADMM）求解相应的优化问题。在ORL、Yale、AR和FERET四个经典人脸数据库上的实验表明，所提方法在高遮挡或高损伤情况下仍保持极高的识别率，验证了其在人脸识别中的强鲁棒性。
原文摘要 · Abstract (English)
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.
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