ملخص الدراسة:

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.

توثيق المرجعي (APA)