A New Facial Expression Recognition Technique using 2-D DCT and Neural Networks Based Decision Tree

Human facial expression recognition (FER) has attracted much attention in recent years because of its importance in realizing highly intelligent human-machine interfaces. In this paper, we propose a new FER technique that utilizes the 2D DCT of full size facial images and a decision tree with feedforward neural network (NN) based nodes. The first NN-based node of the decision tree is designated to separate one group of facial expressions with members "smile" and "surprise" from another group that contains "anger" and "sadness". This node can reduce the confusion between the category members of the two groups. Two NN-based nodes that follow the first node are established for each group to separate their two members. As a result, the original recognition problem with four categories is divided into three subproblems, each having only two members to distinguish. This work is the first trial to use NN, decision tree and 2D DCT simultaneously within a single recognition task. To demonstrate the capability of the proposed recognition technique, we use two databases, including a recently constructed one, which contain 2D front face images of 60 men and 60 women, respectively. Experimental results reveal that the new technique outperforms, on the whole, the simple vector matching and K-means based vector matching techniques and two recently developed methods using fixed-size and constructive neural networks. The mean recognition rates of the new technique have been found as high as 97.5% and 93.8% for these two databases, respectively, which are the best results among those obtained for the same databases.