FACIAL EXPRESSION DETECTION AND RECOGNITION SYSTEM
Liyanage C. De Silva, Prahlad Vadakkepat · 2004
In this paper, the integration of face feature detection and extraction, and facial expression recognition are discussed. In this paper, we propose an algorithm that utilizes multi-stage integral projection to extract facial features. Furthermore, in this project, we propose a statistical approach to process the optical flow data to obtain the overall value for the respective feature region in the face. This approach has eliminated the requirement of accurate identification of the feature boundary. Optical flow computations are utilized to identify the directions and the amount of motions in image sequences that are caused by human facial expressions. The optical flow computation results are processed using Kalman filtering. The filtered results are given to a neural network to realize a mapping into the facial expression space. This technique is used on a set of training and testing face images. Preliminary experiments indicate an accuracy between 60% - 80% on the Kalman filtered data when recognizing four types of expressions: anger, sad, happy and surprise. In an attempt to further improve the recognition results, we proposed a technique to process the optical flow results using a statistical approach instead of using Kalman filtering. The preliminary experiments on this proposal approach produced accuracy between 70% - 100% on the original optical flow results that is better than the Kalman filter technique.