Representation of human faces

Several models for parameterized face representations have been proposed in the last years. A simple coding scheme treats the image of a face as a long vector with each entry coding for the intensity of one single pixel in the image. (e.g. Sirovich & Kirby 1987). Although simple and straitforward, such pixel-based representations have several disadvantages. We propose a representation for images of faces that separates texture and 2D shape by exploiting pixel-by-pixel correspondence between the images. The advantages of this representation compared to pixel-based representations are demonstrated by means of the quality of low-dimensional reconstructions derived from principal component analysis and by means of the performance that simple linear classifier can achieve for sex classification.