3D face pose discrimination using wavelets

This paper describes a robust method for discriminating 3D face pose (face orientation) from a video sequence featuring views of a human head under variable lighting and facial expression conditions. The wavelet transform is used to decompose the image into multiresolution face images containing both spatial and spatial-frequency information. Principal component analysis (PCA) is used to project a low-resolution sub-band face pose onto a pose eigen-space where the first 3 eigen coefficients are found most sensitive to pose and follow a trajectory as the pose changes. Any unknown pose of an query image can then be estimated by finding the Euclidean distance of the first 3 eigen coefficients of the query image from the estimated trajectory. The wavelet transform reduces the computational load on the PCA and makes the algorithm robust against illumination changes and facial expression. An accuracy of 84% was obtained for test images unseen during training under different environment conditions, facial expressions, and even different human subjects