Score Based Biometric Template Selection and Update

A biometric system usually contains two stages: registration and authentication. Most biometric systems capture multiple samples of the same biometric trait at registration. As a result, it is essential to select several samples as templates. This paper proposes two algorithms maximum match scores (MMS) and greedy maximum match scores (GMMS) based on match scores for template selection and update. The proposed algorithms need not involve the specific details about the biometric data. Therefore, they are more flexible and can be used in various biometric systems. The two algorithms are compared with Random and sMDIST on the database of FVC2006DB1A, and the experimental results show that the proposed approaches can improve the accuracy of biometric system efficiently. Based on the maximized score model, we propose two strategies: ONLINE and OFFLINE for templates update. And then we analyze the relationship between them. Preliminary experiments demonstrate that OFFLINE strategy gains better performance.