Real-time facial pose estimation using eigenspace analysis in a low-dimensional projection-domain image representation

Abstract

We introduce eigenspace analysis of image projections and demonstrate that it yields approximately linear representations of facial rotation in the directions up-down and left-right, respectively. The approach uses unsupervised learning-the representation is established even without explicit knowledge of the actual face pose. The method is computationally very inexpensive, as it uses only image projections, a very low-dimensional image representation, and a small number of principal components. In addition, the approach allows us to make effective use of the built-in image projection functions of our artificial retina chips. For a number of applications the method offers thus a fast alternative to more precise and more general, but also more complex methods for determining facial pose.