Deciphering the Face.

We argue that to make robust computer vision algorithms for face analysis and recognition, these should be based on configural and shape features. In this model, the most important task to be solved by computer vision researchers is that of accurate detection of facial features, rather than recognition. We base our arguments on recent results in cognitive science and neuroscience. In particular, we show that different facial expressions of emotion have diverse uses in human behavior/cognition and that a facial expression may be associated to multiple emotional categories. These two results are in contradiction with the continuous models in cognitive science, the limbic assumption in neuroscience and the multidimensional approaches typically employed in computer vision. Thus, we propose an alternative hybrid continuous-categorical approach to the perception of facial expressions and show that configural and shape features are most important for the recognition of emotional constructs by humans. We illustrate how these image cues can be successfully exploited by computer vision algorithms. Throughout the paper, we discuss the implications of these results in applications in face recognition and human-computer interaction.