Facial Feature Detection Using A Hierarchical Wavelet Face Database

WaveBase is a system for detecting features in a face image. It has a database of faces, each with a two-level hierarchical wavelet network. When a new face image is presented to the system for face detection, WaveBase searches its database for the “best face” – the face whose first level wavelet network most closely matches the new face. It also determines an affine transformation to describe any difference in the orientation of the faces. By applying the affine transformation to the position of the features in the best face, approximate feature positions in the new face are found. Second level wavelet networks for each feature are then placed at these approximate positions, and allowed to move slightly to minimize their difference from the new face. This facilitates adjustments in addition to the affine transformation to account for slight differences in the geometry of the best head and the new head. The final position of the wavelet network is WaveBase’s estimate of the feature positions. Experiments demonstrate the benefit of our hierarchical approach. Results compare favorably with existing techniques for feature localization.