Locating human faces in a cluttered scene

In this paper, we present two new schemes for finding human faces in a phot ograph. The first scheme adopts a distribution-based model approach to face -finding. Distributions of the face and the face-like manifolds are approxi mated using higher order statistics (HOS) by deriving a series expansion of the density function in terms of the multivariate Gaussian and the Hermite polynomials in an attempt to get a better approximation to the unknown ori ginal density function. An HOS-based data clustering algorithm is then prop osed to facilitate the decision process. The second scheme adopts a hidden Markov model (HMM) based approach to the face-finding problem. This is an u nsupervised scheme in which face-to-nonface and nonface-to-face transitions are learned by using an HMM. The HMM learning algorithm estimates the HMM parameters corresponding to a given photograph and the faces are located by examining the optimal state sequence of the HMM. We present experimental r esults on the performance of both schemes. A training data base of face ima ges was constructed in the laboratory. The performances of both the propose d schemes are found to be quite good when measured with respect to several standard test face images. (C) 2000 Academic Press.