Principal Component Analysis of Multi-view Images for Viewpoint-Independent Face Recognition

We consider the problem of recognizing a specific human face in different poses (viewing direction) when only one frontal face image exists in the face database. To solve this problem, prior knowledge is learned by using principal component analysis on a set of multi-view images to obtain aligned principal components. They are used together with the idea of linear object classes to synthesize a virtual view of the frontal face from a given face image taken from a different viewing direction. The estimated virtual frontal view is then compared with the stored frontal face images in the face database to identify the person. Experimental results are shown using face images captured from different viewpoints.