A Machine Learning Algorithm of Human-Computer-Interface Application-An AdaRank Model Approach to Facial Expression Recognition

A completely automatic facial expression recognition system is presented in this paper, which consists of three main procedures. The first is based on skin color blocks and geometrical properties applied to eliminate the skin color regions that do not belong to the face in the HSV color space. Than we find proper ranges of eyes, mouth, and eyebrows according to the positions of pupils and center of a mouth. Subsequently, we perform both the edge detection and binarization operations on the above ranged images to obtain 16 landmarks. After manipulating these landmarks, 16 characteristic distances are the facial feature produced to represent a kind of expressions. Finally, we subtract the 16 characteristic distances of a neutral face from the 16 characteristic distances of a certain expression to acquire its 16 displacement values fed to a classifier with an incremental learning scheme, which can identify six kinds of expressions: joy, anger, surprise, fear, sadness, and neutral. We choose the AdaRank model as the core technique to implement our strong facial expression classifier. Our model, referred to as AdaRank, repeatedly constructs classifiers on the basis of re-weighted training data and finally linearly combines the classifiers for making ranking predictions. Through conducting many experiments, the statistics of performance reveals that the accuracy rate of our facial expression recognition system reaches more than 95%.