In this study

we propose a new kernel discriminant for learning and recognition of image sets using canonical correlation. Each image set is mapped into a high-dimensional feature space. The corresponding kernel space is then constructed by a kernel linear discriminant analysis.The similarity of two kernel subspaces is assessed by calculating the canonical difference between them. According to the kernel Fisher discriminant

a Kernel Discriminant Analysis of Canonical Correlation algorithm is derived to establish the correlation between the kernel subspaces based on the ratio of the canonical differences of the between-classes to those of the within-classes. The experimental results on the ORL

NUST603

FERNT and XM2VTS database demonstrate that the proposed method can efficiently extract the features of the images. Moreover

the recognition rate of the proposed algorithm outperforms DCC and KDT.