Finger vein identification is a new biometric identification technology. While many existing works approach the problem by using shape matching which is the generative method, in this paper, we introduce a joint discriminative and generative algorithm for the task. Our method considers both the discriminative appearance of local image patches as well as their generative spatial layout. The method is based on the popular vocabulary tree model, where we utilize the hidden leaf node layer to calculate a generative confidence to weight the discriminative vote from the leaf node. The training process remains the same as building a conventional vocabulary tree, while the prediction process utilizes a proposed point set matching method to support non-parametric patch layout matching. In this way, the entire model retains the efficiency of the vocabulary tree model, which is much lighter than other similar models such as the constellation model (Fergus et al., 2003). The overall estimation follows the Bayesian theory. Experimental results show that our proposed joint model outperformed the purely generative or discriminative counterpart, and can offer competitive performance than existing methods for both the vein authentication and recognition tasks. 
Discriminative and generative vocabulary tree: With application to vein image authentication and recognition