关键词: 掌纹识别, 核主成分分析, 欧式距离, 支持向量机, 特征提取

Abstract: Principal Component Analysis（PCA） or Kernel Principal Component Analysis（KPCA） can only extract the linear or nonlinear features of palmprint, and single classifier recognition rate is very low, this paper proposes a two level classifier for palmprint recognition based on subspace features. Firstly, the PCA and KPCA are used to extract the linear or nonlinear features of palmprint, respectively, and the best fusion coefficient can be calculated by making the total distance of between-classes largest to get the optimal features of palmprint image, the Euclidean distance metric method is used to recognize palmprint image, if the palmprint image category is clearly, the recognition result is obtained, otherwise the palmprint image is put into support vector machine to recognize. Polyu palmprint image library is used to test the performance, the results show that, compared with other palmprint recognition methods, the proposed method has improved the palmprint recognition rate and recognition speed, and false accept rate and false reject rate are reduced.

Key words: palmprint recognition, kernel principal component analysis, Euclidean distance, support vector machine, feature extraction