Facial Expression Recognition Techniques Using Constructive Feedforward Neural Networks and K-Means Algorithm

In this paper two facial expression recognition (FER) techniques are proposed. Lower-frequency 2-D DCT coefficients of binarized edge images are utilized in both methods as features for recognition. The first approach uses a constructive one-hidden-layer (OHL) feedforward neural network (OHL-NN) and the second approach is based on the K-means algorithm as classifiers. The 2-D DCT is used to compress the binarized edge images to capture the important features for recognition. Facial expression “neutral” is regarded as a subject of recognition in addition to two other expressions, “smile” and “surprise”. The two proposed recognition techniques are applied to two databases which contain 2-D front face images of 60 men (database (a)) and 60 women (database (b)), respectively. Experimental results reveal that the proposed two techniques yield performances that are comparable to or better than that of two other recognition methods using vector matching and fixed-size BP-based NNs, respectively. The first proposed method yields testing recognition rates as high as 100% and 95%, and the second one achieves as high as 100% and 98.33%, for databases (a) and (b), respectively.