In this paper, we investigate recognition of human faces in a meeting room. The major challenges of identifying human faces in this environment include low quality of in-put images, poor illumination, unrestricted head poses and continuously changing facial expressions and occlusion. In order to address these problems we propose a novel algorithm, Dynamic Space Warping (DSW). The basic idea of the algorithm is to combine local features under certain spatial constraints. We compare DSW with the eigenface approach on data collected from various meetings. We have tested both front and profile face images and images with two stages of occlusion. The experimental results indicate that the DSW approach outperforms the eigenface approach in both cases.