一种改进的BDPCA掌纹识别方法

在小样本的情况下，BDPCA算法中采用以训练样本的平均值作为样本分布中心，所得的特征值不一定是最优的。为此，提出了一种基于样本散度矩阵的改进BDPCA掌纹识别算法。该算法采用训练样本的K值矩阵替代训练样本的均值矩阵，构建相应的总体散度矩阵。在PolyU和CASIA掌纹库上的实验结果证明，该方法的最优识别率高于传统的BDPCA算法。

Improved BDPCA method for palmprint recognition

Under the condition of small sample size, the average of all training samples used in the Bi-Directional PCA algorithm is the scatter center of the samples. This algorithm can not guarantee the optimality of the eigenvalues. In order to solve this problem, this paper proposes an improved BDPCA palmprint identification algorithm which is based on sample scatter matrix. To reconstruct the overall scatter matrix, the algorithm adopts the K-values matrix of the training samples instead of the average matrix of the training samples. The algorithm is tested using PolyU and CASIA. The results show that the improved method is more optimal in recognition rate than the traditional BDPCA.