Categorical Perception in Facial Emotion Classification

We present an automated emotion recognition system that is capable of identifying six basic emotions (happy, surprise, sad, angry, fear, disgust) in novel face images. An ensemble of simple feed-forward neural networks are used to rate each of the images. The outputs of these networks are then combined to generate a score for each emotion. The networks were trained on a database of face images that human subjects consistently rated as portraying a single emotion. Such a system achieves 86% generalization on novel face images (individuals the networks were not trained on) drawn from the same database. The neural network model exhibits categorical perception between some emotion pairs. A linear sequenceof morph images is created between two expressions of an individual’s face and this sequence is analyzed by the model. Sharp transitions in the output response vector occur in a single step in the sequence for some emotion pairs and not for others. We plan to us the model’s response to limit and direct testing in determining if human subjects exhibit categorical perception in morph image sequences.