Much progress has been made to understand human cognition. Yet, little is know about face perception. Facial expressions of emotions is a clear example of this limitation. It has been established how the underlying muscles move in response to felt emotions, but little is known on how these constructs are interpreted by the visual system and how to build robust computer vision systems that emulate human perception. In this talk, we will review the recent literature on this subject and proposes a model for the perception of facial expressions of emotion. We will show that different emotions have diverse uses in human behavior/cognition, suggesting that different mechanisms are in play when recognizing distinct emotions. This is in contradiction to the continuous models in cognitive science and the multidimensional approaches typically defined in computer vision. We propose an alternative categorical approach to the perception of facial expressions and show that configural and shape features are most important for the recognition of emotional constructs such as sadness, anger, surprise and joy. We will emphasize the implications of these results on the construction of computer vision systems for the automatic analysis and recognition of facial expressions and its importance in human-computer interaction systems. We will also discuss the implications of these findings in computational models of other face perception tasks, e.g., identity, gender and grammar.

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