Improved Prototype Selection in Synergetic Pattern Recognition to Recognize Human Face Expressions

The prototype selection plays critical roles in synergetic pattern recognition (SPR). K-means clustering is widely adopted to determine appropriate prototypes in SPR. However, the selection of initial cluster centers significantly affects clustering results. We propose an improved k-means clustering to handle this challenge. According to inner-class distances among samples within the same cluster, we will dynamically adjust inter-class distances among clusters. Initial cluster centers will then be highly representative in that they are distributed among as many samples as possible. Consequently, local optima that are common in k-means clustering can be effectively reduced. After we obtain final cluster centers output from the improved k-means clustering, we then use these centers as the prototype vector to train a synergetic neural network (SNN), which will be utilized to recognize human face expressions. Experimental results demonstrate that our algorithm greatly improves the accuracy in recognizing face expressions and, in a more efficient manner.