关键词: 掌纹识别, 支持向量机, 主成分分析, 统一选择, 粒子群优化算法

Abstract: In order to enhance the palmprint recognition performance, it proposes a novel palmprint recognition model based on simultaneously selecting features and classifier parameters according to relation between the dimensions of the Principal Component Analysis（PCA） and parameters of Support Vector Machines（SVM）. The palmprint image is preprocessed, and then the dimensions of PCA and parameters of SVM are taken as a particle, the optimal palmprint features and parameters of SVM are obtained simultaneously by information exchange and cooperation of particle swarms, the optimal palmprint recognition model is established based on the selected dimensions and parameters, the performance of model is tested by Po1yU palmprint data. The results show that the proposed model can obtain recognition rates of the palmprint 94%, the prediction results are significantly better than reference models which features and classifier parameters are selected separately.

Key words: palmprint recognition, support vector machine, principal component analysis, simultaneously selection, particle swarm optimization algorithm