Improved facial expression recognition with trainable 2-D filters and support vector machines

Facial expression is one way humans convey their emotional states. Accurate recognition of facial expressions is essential in perceptual human-computer interface, robotics and mimetic games. This paper presents a novel approach to facial expression recognition from static images that combines fixed and adaptive 2-D filters in a hierarchical structure. The fixed filters are used to extract primitive features. They are followed by the adaptive filters that are trained to extract more complex facial features. Both types of filters are non-linear and are based on the biological mechanism of shunting inhibition. The features are finally classified by a support vector machine. The proposed approach is evaluated on the JAFFE database with seven types of facial expressions: anger, disgust, fear, happiness, neutral, sadness and surprise. It achieves a classification rate of 96.7%, which compares favorably with several existing techniques for facial expression recognition tested on the same database.