A PSO- SVM Lips Recognition Method Based on Active Basis Model
Chih‐Yu Hsu, Chih-Hung Yang, Yung‐Chih Chen, Min-chian Tsai · 2010
The paper proposed a novel method for lip recognition based on Active Basis Model (ABM). There are four stages in a flowchart of this novel method. At the first stage the deformable templates of lip images is obtained. The lip images of deformable templates are obvious open or closed. The second stage is to obtain the deformed template of each testing images. The third stage, the difference between the deformable template and deformed template is calculated and used as a feature vector. Finally, the support vector machine (SVM) is use to classify the feature vector. SVM is a supervised learning to be a classifier for lip recognition. It is necessary to set and parameters, which are the two factors affecting quality of the model, when establishing SVM method. Hence, in terms of parameters selections, the researcher adopted PSO (Particle Swarm Optimization, PSO) algorithm in this research to set the best parameters combination and then incorporated into the SVM to obtain the classified results. In this paper, the novel method which utilizes PSO algorithm to select the parameters and automatically is called PSO-SVM method. There are 1000 face images in BioID face database used for the experiment. The experimental results show that PSO-SVM method can be a more accurate model to recognize the lip images.