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.

翻译：世界各地生物鉴别技术部署的范围、规模和数量不断增加,这突出表明需要研究技术,促进高效可靠的生物鉴别问询;这项工作提出了生物鉴别数据库索引化方法,依靠面部图像的信号级融合(变形)来创建多阶段的数据结构和检索协议。通过连续预先筛选潜在候选身份清单,拟议方法使得有可能减少生物鉴别模板比较的必要数量,以完成生物鉴别交易。拟议方法在公开数据库中利用开放源码和商业现成识别系统进行广泛评价。结果显示,使用拟议方法,计算工作量可以减少到30%左右,同时在封闭式和开放式识别假设中,完全保持基线、详尽搜索检索的生物鉴别工作。