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Confidence-based Rank-level fusion for Audio-visual person identification system

Alam, M., Bennamoun, M., Togneri, R. and Sohel, F. (2014) Confidence-based Rank-level fusion for Audio-visual person identification system. In: 3rd International Conference on Pattern Recognition Applications and Methods (ICPRAM 2014), 6 - 8 March 2014, ESEO, Angers, Loire Valley, France


A multibiometric identification system establishes the identity of a person based on the 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 its gallery. In rank-level fusion, ranked lists from different sub-systems are combined to reach the final decision about an identity. However, the state-of-art rank-level fusion methods consider that all sub-systems perform equally well in any conditions. In practice, the probe data may be affected by different degradations (e.g., illumination and pose variation on the face image, environmental noise etc.) and thus affect the overall recognition accuracy. In this paper, robust confidence-based rank-level fusion methods are proposed by using confidence measures for all participating sub-systems. Experimental results show that the confidence-based approach of rank-level fusion achieves higher recognition rates than the state-of-art.

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