Multi-biometric cohort analysis for biometric fusion

Biometric matching decisions have traditionally been made based solely on a score that represents the similarity of the query biometric to the enrolled biometric(s) of the claimed identity. Fusion schemes have been proposed to benefit from the availability of multiple biometric samples (e.g., multiple samples of the same fingerprint) or multiple different biometrics (e.g., face and fingerprint). These commonly adopted fusion approaches rarely make use of the large number of non-matching biometric samples available in the database in the form of other enrolled identities or training data. In this paper, we study the impact of combining this information with the existing fusion methodologies in a cohort analysis framework. Experimental results are provided to show the usefulness of such a cohort-based fusion of face and fingerprint biometrics.