Recognition of facial expressions using 2D DCT and neural network

Abstract

The recognition of human facial expressions has been expected to have applications in various fields, such as psychology, engineering, and so forth, and many techniques have been proposed to date. However, most of the techniques are based on the motions of local feature-bearing blocks, such as the eyes and mouth, which are supposed to be closely associated with facial expressions. It is thus required to segment these blocks from the face image and to track their motions in real-time applications, resulting in high complexity. Furthermore, not all information on the motions of the facial expressions has been utilized. In this paper, a new recognition technique is proposed, 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 differences of the DCT coefficients in the lower frequency area between neutral and expression-bearing images are given to a neural network to realize a mapping 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 persons used for network training and the remaining images for testing. The recognition rate is 100% for the training images. A maximum recognition rate of 95% is achieved for the testing images. © 1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(7): 1–11, 1999