Parameter adjustment of finger vein recognition algorithms

Abstract

Finger vein recognition is a biometric method utilizing the vein patterns inside one's fingers for personal identification. Recognition algorithm is the key part of a finger vein recognition system, dominating the system performance. There are usually a lot of parameters in algorithms, and different values of the parameters could lead to different system performance so that it is essential to set a proper value for each parameter in practice. In this paper, we conduct a set of experiments to study how the parameters influence the performance measured by equal error rate. We have made two observations from the results: 1.When an algorithm is applied on a dataset, the performance differs a lot as the parameter value changes even in a small range; 2.When an algorithm is applied on different datasets, the performance differs a lot, in other words, the optimized parameter value combination that maximizes the system performance differs significantly. These two observations reveal the importance of parameter adjustment in finger vein recognition. So this paper proposes two solutions: search algorithm and estimation by subset, which are fast, accurate and scalable methods to find the best parameters. The experiment results prove the effectiveness of our methods.