In this paper, we present two new schemes for finding human faces in a photograph. The first scheme adopts a distribution-based model approach to face-finding. Distributions of the face and the face-like manifolds are approximated 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 original density function. An HOS-based data clustering algorithm is then proposed to facilitate the decision process. The second scheme adopts a hidden Markov model (HMM) based approach to the face-finding problem. This is an unsupervised 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 results on the performance of both schemes. A training data base of face images was constructed in the laboratory. The performances of both the proposed schemes are found to be quite good when measured with respect to several standard test face images.