Application of Improved CPSO-SVM Approach in Face Recognition

Support vector machine (SVM) has become a popular classification tool, but training SVM consumes large memory and computation time. Traditional methods can not overcome above shortcomings. This paper presents a novel SVM training method based improved chaotic particle swarm optimization (CPSO) algorithm. Firstly, a new chaotic search model using improved circle map is introduced. Then this new model is introduced into particle swarm optimization (PSO). Finally, the detail training SVM algorithm using this improved CPSO algorithm is presented. Experimental results on face database show that the presented SVM method optimized by CPSO can achieve higher recognition performance.