Improving Biometric Identification Through Score Level Face Fingerprint Fusion

Multi-modal biometric fusion is more accurate and reliable compared to recognition using a single biometric modality. However, most existing fusion approaches neglect the influence of the qualities of the biometric samples in information fusion. Our goal is to advance the state-of-the-art in biometric fusion technology by providing a more universal and more accurate solution for personal identification and verification with predictive quality metrics. In this work, we developed score-level multi-modal fusion algorithms based on predictive quality metrics and employed them for the task of face and fingerprint biometric fusion In this paper the performance of sum rule-based score level fusion are examined. Before fusion of sum rule, normalization is done by using any one technique like min-max normalization, z score normalization and tanh estimator’s normalization. In this paper min max normalization is used for normalization.