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 multistage 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.

Model Construction of Water Pollution Prevention Project Based on Small Sample Learning and Data Fusion

“…Data fusion is an important and effective solution for water pollution prevention projects with a large number of samples [13][14]. Using different models such as multi-source sensors and embedded computing algorithms, it is observed that there is a certain degree of correlation between the target information and the original signal.…”