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 of faces as well as clutter. Our experiments indicate that the proposed algorithm outperforms conventional template-based methods such as matched filter and eigenface methods.
['Lingmin Meng', 'Truong Q. Nguyen', 'David A. Castanon']
An image-based Bayesian framework for face detection