Improved performance in facial expression recognition using 32 geometric features

Automatic facial expression recognition is one of the most interesting problem as it impacts on important applications in human-computer interaction area. Many applications in this field require real-time performance but not all the approach are suitable to satisfy this requirement. Geometrical features are usually the most light in terms of computational load but sometimes they exploits a huge number of features and do not cover all the possible geometrical aspect. In order to face up this problem, we propose an automatic pipeline for facial expression recognition that exploits a new set of 32 geometric facial features from a single face side covering a wide set of geometrical peculiarities. As a results, the proposed approach showed a facial expression recognition accuracy of 95,46% with a six-class expression set and an accuracy of 94,24% with a seven-class expression set.