Signal-level Fusion for Indexing and Retrieval of Facial Biometric Data
This work addresses the need for faster and reliable biometric identification in large-scale deployments, though it appears incremental as it builds on existing fusion and indexing techniques.
The paper tackles the problem of efficient biometric identification by proposing a signal-level fusion method for indexing facial databases, which reduces computational workload to around 30% while maintaining baseline performance in closed-set and open-set scenarios.
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%, while the biometric performance of a baseline exhaustive search-based retrieval is fully maintained, both in closed-set and open-set identification scenarios.