Pose invariant method for emotion recognition from 3D images

Information about the emotional state of a person can be inferred from facial expressions. Emotion recognition has become an active research area in recent years in various fields such as Human Robot Interaction (HRI), medicine, intelligent vehicle, etc., The challenges in emotion recognition from images with pose variations, motivates researchers to explore further. In this paper, we have proposed a method based on geometric features, considering images of 7 yaw angles (-45°,-30°,-15°,0°,+15°,+30°,+45°) from BU3DFE database. Most of the work that has been reported considered only positive yaw angles. In this work, we have included both positive and negative yaw angles. In the proposed method, feature extraction is carried out by concatenating distance and angle vectors between the feature points, and classification is performed using neural network. The results obtained for images with pose variations are encouraging and comparable with literature where work has been performed on pitch and yaw angles. Using our proposed method non-frontal views achieve similar accuracy when compared to frontal view thus making it pose invariant. The proposed method may be implemented for pitch and yaw angles in future.