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The growing scope, scale, and number of biometric deployments around theworld emphasise the need for research into technologies facilitating efficientand reliable biometric identification queries. This work presents a method ofindexing biometric databases, which relies on signal-level fusion of facialimages (morphing) to create a multi-stage data-structure and retrievalprotocol. By successively pre-filtering the list of potential candidateidentities, the proposed method makes it possible to reduce the necessarynumber of biometric template comparisons to complete a biometric identificationtransaction. The proposed method is extensively evaluated on publicly availabledatabases using open-source and commercial off-the-shelf recognition systems.The results show that using the proposed method, the computational workload canbe reduced down to around 30%, while the biometric performance of a baselineexhaustive search-based retrieval is fully maintained, both in closed-set andopen-set identification scenarios.

Signal-level Fusion for Indexing and Retrieval of Facial Biometric Data