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Face recognition and identification is a very active research area nowadays due to its importance in both human computer and social interaction. Psychological studies suggest that face recognition by human beings can be featural, configurational, and holistic. In this paper, by incorporating spatially structured features into a histogram-based face-recognition framework, we intend to pursue consistent performance of face recognition. In our proposed approach, while diffusion distance is computed over a pair of human face images, the shape descriptions of these images are built using Gabor filters that consist of a number of scales and levels. It demonstrates that the use of perceptual features by Gabor filtering in combination with diffusion distance enables the system performance to be significantly improved, compared to several classical algorithms. The oriented Gabor filters lead to discriminative image representations that are then used to classify human faces in the database.