A Modular Approach for Facial Expression Recognition using HSOG

Automatic facial expression recognition is one of the most recently topic in aspect of behaviour analysis and human computer interaction (HCI). Difficulty with facial expression recognition system is to implement generic model. Same facial expression may vary across humans, even this is true for the same person when the expression is displayed in different situations. This paper proposed the local image descriptor that extracts the histogram of second order gradients (HSOG), which capture the local curvatures of differential geometry. The shape index is computed from the curvatures and its different values correspond to different shapes. In case of facial expression recognition using full face images, if any portion of the face image is distorted, it may reflect on the recognition performance. Humans have the capability to recognize faces even by looking at some parts of the face. An attempt has been made to replicate the same on machines by only considering some of the informative regions of the face like eyes, nose, lip and forehead. Facial expression recognition experiments have been performed on some benchmark databases, Better recognition rates were achieved compared to other existing approaches.