CRISP scientists (P. Drozdowski, Dr. C. Rathgeb, Prof. Dr. C. Busch) from Hochschule Darmstadt investigated the topic of efficient biometric identification from both the academic and industry perspective. The work was conducted in the context of LOEWE-3 sponsored project BioBiDa — Biometrics and Big Data and resulted in an article entitled “Computational Workload in Biometric Identification Systems: An Overview” being published with an open access license in the IET Biometrics journal. The article can be freely accessed by following this link.
The article provides a comprehensive overview of methods for efficient biometric identification irrespective of the chosen biometric characteristic. Accordingly, its contributions are threefold:
- A taxonomy, which conceptually categorises the computational workload reduction methods in biometric identification.
- A comprehensive survey of the existing methods reported in the scientific literature and organised by the relevant high-level concepts from the aforementioned taxonomy.
- A diversified discussion pertaining to relevant technical and practical considerations and trade-offs, an industry perspective, and open research issues/challenges.
Computational workload is one of the key challenges in biometric identification systems. The naïve retrieval method based on an exhaustive search becomes impractical with the growth of the number of the enrolled data subjects. Consequently, in recent years, many methods with the aim of reducing or optimising the computational workload, and thereby speeding-up the identification transactions, in biometric identification systems have been developed. In this article, a taxonomy for conceptual categorisation of such methods is presented, followed by a comprehensive survey of the relevant academic publications, including computational workload reduction and software/hardware-based acceleration. Lastly, the pertinent technical considerations and trade-offs of the surveyed methods are discussed, along with an industry perspective, and open issues/challenges in the field.