Multi-Pose Facial Expression Recognition Based on SURF Boosting

Today Human Computer Interaction (HCI) is one of the most important topics in machine vision and image processing fields. The ability to handle multi-pose facial expressions is important for computers to understand affective behavior under less constrained environment. In this paper, we propose a SURF (Speeded-Up Robust Features) boosting framework to address challenging issues in multi-pose facial expression recognition (FER). Local SURF features from different overlapping patches are selected by boosting in our model to focus on more discriminable representations of facial expression. And this paper proposes a novel training step during boosting. The experiments using the proposed method demonstrate favorable results on RaFD and KDEF databases.