An image-based Bayesian framework for face detection

In this paper, we present a novel approach for frontal face detection in gray-scale images. We represent both faces and clutter by using two-dimensional wavelet decomposition. To characterize the statistical dependency between different levels of wavelet, we introduce a Hidden Markov Model (HMM), in which a number of discrete: states at each level capture the diversity effaces as well as clutter. Our experiments indicate that the proposed algorithm outperforms conventional template based methods such as matched filter and eigenface methods.