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Confidence-based Rank-level Fusion for Audio-visual Person Identification System

A multibiometric identification system establishes the identity of a person based on the input biometric data presented to its sub-systems. Each sub-system compares the features extracted from the input against the templates of all identities stored in gallery. The best matched identity is ranked highest in the ranked list. In rank-level fusion, the ranked lists from different sub-systems are combined to reach a final decision. However, the state-of-the-art rank-level fusion methods consider that all sub-systems are equally reliable in terms of classifying the probe data. In practice, the probe data may be affected by different sources of degradation (e.g., illumination and pose variation on the face image, environmental noise) and thus affecting the overall recognition accuracy. In this paper, robust rank-level fusion methods (e.g., confidence based highest rank and Borda count) are proposed by using confidence measures for each sub-system in the decision making process. Experimental results show that the proposed confidence based rank-level fusion achieved higher recognition rates than state-of-the-art rank-level fusion methods.