Gabor wavelet networks for efficient head pose estimation

In this paper we first introduce the Gabor Wavelet Network (GWN) as a model-based approach for effective and efficient object representation. GWNs combine the advantages of the continuous wavelet transform with RBF networks. They have additional advantages such as invariance to some degree with respect to affine deformations. The use of Gabor filters enables the coding of geometrical and textural object features. Gabor filters as a model for local object features ensure considerable data reduction while at the same time allowing any desired precision of the object representation ranging from sparse to photo-realistic representation. As an application we present an approach for the estimation of head pose based on the Gabor Wavelet Networks. Feature information is encoded in the wavelet coefficients. An artificial neural network is then used to compute the head pose from the wavelet coefficients.