  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.
