| Abstract |
This paper addresses one of the main challenges of face recognition (FR): facial occlusions. Currently, the human brain is the most robust known FR approach towards partially occluded faces. Nevertheless, it is still not clear if humans recognize faces using a holistic or a component-based strategy, or even a combination of both. In this paper, three different approaches based on Principal Component Analysis (PCA) are analyzed. The first one, a holistic approach, is the well-known Eigenface approach. The second one, a component-based method, is a variation of the Eigen-features approach, and finally, the third one, a near-holistic method, is an extension of the Lophoscopic Principal Component Analysis (LPCA). So the main contributions of this paper are: The three different strategies are compared and analyzed for identifying partially occluded faces and furthermore it explores how a priori knowledge about present occlusions can be used to improve the recognition performance. |
