Most of the available research on face recognition has been performed using gray scale imagery. This paper presents a novel two-pass face recognition system that uses a Multispectral Random Field Texture Model, specifically the Multispectral Simultaneous Auto Regressive (MSAR) model, and illumination invariant color features. During the first pass, the system detects and segments a face from the background of a color image, and confirms the detection based on a statistically modeled skin pixel map and the elliptical nature of human faces. In the second pass, the face regions are located using the same image segmentation approach on a subspace of the original image, biometric information, and spatial relationships. The determined facial features are then assigned biometric values based on anthropometrics, and a set of vectors is created to determine similarity in the facial feature space.