Face recognition approaches that use subspace projection are heavily related to basis images, especially in the case of partial occlusion. To improve the recognition performance, the occlusion should be excluded from the test image during the recognition process. In terms of similarity with image reconstruction, the proposed approach aims at representing the whole face image based on facial subregion. In this respect, face representation can be considered as an inverse problem. The Tikhonov regularization approach is combined with independent component analysis (ICA) in order to obtain the image parameter from the occluded image, where this parameter is compared with that trained by ICA. The combined algorithm is named as RegICA and is performed on face images in the AR Face Database. Cumulative match characteristics was taken as a measure for evaluating the performance of RegICA with occlusion. Compared with ICA on facial occlusion problem, it was found that the proposed approach outperforms ICA. In addition, it has the ability to recognize faces using any facial subregion even if it is small. Furthermore, it is shown that RegICA is not time-consuming, and it outperforms some of the recent approaches in terms of accuracy.

使用子空间投影的人脸识别方法与基础图像密切相关，尤其是在部分遮挡的情况下。为了提高识别性能，在识别过程中应该从测试图像中排除遮挡。在与图像重建的相似性方面，所提出的方法旨在基于面部子区域表示整个面部图像。在这方面，人脸表示可以被认为是一个逆问题。Tikhonov 正则化方法与独立分量分析 (ICA) 相结合，以便从被遮挡的图像中获取图像参数，并将该参数与 ICA 训练的参数进行比较。组合算法被命名为 RegICA，并在 AR 人脸数据库中的人脸图像上执行。累积匹配特征被用作评估具有遮挡的 RegICA 性能的度量。在面部遮挡问题上与 ICA 相比，发现所提出的方法优于 ICA。此外，它具有使用任何面部子区域识别面部的能力，即使它很小。此外，事实证明，RegICA 并不耗时，并且在准确性方面优于最近的一些方法。