A new facial expression recognition technique using 2D DCT and k-means algorithm

Facial expression recognition plays a vital role in realizing a highly intelligent human-machine interface, and has recently attracted much attention. In this paper, we propose a new facial expression recognition method that utilizes the 2D DCT, k-means algorithm and vector matching. This technique is based on two main intuitive ideas: (i) complicated facial expression categories such as "anger" and "sadness", may be divided into several subcategories with different subfeature spaces where the recognition task can be performed with higher accuracy and (ii) the k-means algorithm may be used to cluster these subcategories. A new image database with five facial expressions (neutral, smile, anger, sadness, surprise) of 60 women was constructed using a computationally efficient projection-based technique. Experimental results using the new database and an existing one (60 men) reveal that the new technique outperforms the standard vector matching technique and two recently developed methods using fixed-size and constructive one-hidden-layer neural networks. The mean recognition rate can be as high as 95% for the two databases.