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

Abstract

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 searchbased retrieval is fully maintained, both in closed-set and open-set identification scenarios.

Feature Fusion Methods for Indexing and Retrieval of Biometric Data: Application to Face Recognition with Privacy Protection

“…Consequently, it is possible to make a robust pre-selection of a candidate short-list candidates ← select candidates with best scores 6: end for 7: return candidates to be passed onto the next level of the cascade. In a successive manner, which is conceptually similar to the previous works on multi-modal and signal-level fusion-based cascades of Drozdowski et al [59], [61], the candidate short-list shrinks at each level, thus resulting in fewer template comparisons being made and hence in computational workload reduction. The computational workload (W ) [30] of the proposed retrieval scenario can be obtained using the following formula:…”

“…In [59], a multi-biometric cascade has been proposed with the aim of successively filtering the candidate short-lists based on score-level information. Similar concepts were utilised in [60], [61], where a signal-level fusion (i.e. morphing, see e.g.…”