Signal-level Fusion for Indexing and Retrieval of Facial Biometric Data
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The growing scope, scale, and number of biometric deployments around the world emphasise the need for research into technologies facilitating efficient and reliable biometric identification queries. This work presents a method of indexing biometric databases, which relies on signal-level fusion of facial images (morphing) to create a multi-stage data-structure and retrieval protocol. By successively pre-filtering the list of potential candidate identities, the proposed method makes it possible to reduce the necessary number of biometric template comparisons to complete a biometric identification transaction. The proposed method is extensively evaluated on publicly available databases using open-source and commercial off-the-shelf recognition systems. The results show that using the proposed method, the computational workload can be reduced down to around 30 exhaustive search-based retrieval is fully maintained, both in closed-set and open-set identification scenarios.
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