A major challenge for face recognition algorithms lies in the variance faces undergo while changing pose. This problem is typically addressed by building view dependent models based on face images taken from predefined head poses. However, it is impossible to determine all head poses be-forehand in an unrestricted setting such as a meeting room, where people can move and interact freely. In this paper, we present an approach to pose invariant face recognition. We employ Gaussian mixture models to characterize human faces and model pose variance with different numbers of mixture components. The optimal number of mixture components for each person is automatically learned from training data by growing the mixture models. The proposed algorithm is tested on real data recorded in a meeting room. The experimental results indicate that the new method out-performs standard eigenface and Gaussian mixture model approaches. Our algorithm achieved as much as 42% error reduction compared to the standard eigenface approach on the same test data.