Robust facial expressions recognition using 3D average face and ameliorated adaboost

One of the most crucial techniques associated with Computer Vision is technology that deals with facial recognition, especially, the automatic estimation of facial expressions. However, in real-time facial expression recognition, when a face turns sideways, the expressional feature extraction becomes difficult as the view of camera changes and recognition accuracy degrades significantly. Therefore, quite many conventional methods are proposed, which are based on static images or limited to situations in which the face is viewed from the front. In this paper, a method that uses Look-Up-Table (LUT) AdaBoost combining with the three-dimensional average face is proposed to solve the problem mentioned above. In order to evaluate the proposed method, the experiment compared with the conventional method was executed. These approaches show promising results and very good success rates. This paper covers several methods that can improve results by making the system more robust.