Recognition of facial expressions using 2D DCT and neural network

The recognition of human facial expressions has been expected to have appli cations in various fields, such as psychology, engineering, and so forth, a nd many techniques have been proposed to date. However, most of the techniq ues are based on the motions of local feature-bearing blocks, such as the e yes and mouth, which are supposed to be closely associated with facial expr essions. It is thus required to segment these blocks from the face image an d to track their motions in real-time applications, resulting in high compl exity. Furthermore, not all information on the motions of the facial expres sions has been utilized. In this paper, a new recognition technique is prop osed, which uses the 2D DCT of the entire facial image and a neural network . The segmentation of local feature-bearing blocks is not needed. The differe nces of the DCT coefficients in the lower frequency area between neutral an d expression-bearing images are given to a neural network to realize a mapp ing into the facial expression space. The new technique has been applied to a database of normalized facial images of 60 persons, with images of 40 pe rsons used for network training and the remaining images for testing. The r ecognition rate is 100% for the training images. A maximum recognition rate of 95% is achieved for the testing images. (C) 1999 Scripta Technica.