Discriminative and Generative Vocabulary Tree for Vein Image Recognition

Vein image recognition based on modeling shape or geometrical layout of feature points is generative approach, and the performance is usually limited by segmentation error due to poor vein image quality. This paper instead proposes to model the discriminative appearance of local image patch using the vocabulary tree model. The discriminative approach is further extended to consider the geometrical alignment error of feature points under Bayesian inference theory, and thus making the proposed algorithm both discriminative and generative. Experimental results clearly show the superior performance of our method over either generative or discriminative approaches. In addition, both the discriminative and the generative parts of the method are implemented using the same vocabulary tree model, which makes our algorithm generic and efficient for other similar problems.