CAJ | 학술논문

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。
재소양본적정황하，BDPCA산법중채용이훈련양본적평균치작위양본분포중심，소득적특정치불일정시최우적。위차，제출료일충기우양본산도구진적개진BDPCA장문식별산법。해산법채용훈련양본적K치구진체대훈련양본적균치구진，구건상응적총체산도구진。재PolyU화CASIA장문고상적실험결과증명，해방법적최우식별솔고우전통적BDPCA산법。
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 sam-ple scatter matrix. To reconstruct the overall scatter matrix, the algorithm adopts the K-values matrix of the training sam-ples 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.