Accelerating fingerprint identification using FPGA for large-scale applications

• Published in: Journal of parallel and distributed computing Vol. 141; pp. 35 - 48
• Main Authors: Shafiq, Mohsin, Taj, Imtiaz A., Ghafoor, Mubeen, Tariq, Syed Ali, Abbas, Assad, Zomaya, Albert Y.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01-07-2020
• Subjects: Biometrics; Fingerprint identification; FPGA; Parallel processing; Biometrics; Parallel processing; Fingerprint identification; FPGA
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2020.03.007

Fingerprint-based human authentication has shown great potential for civil, forensic and corporate security applications in recent years. For large-scale databases, the complexity of the identification system increases and implementing these systems on general-purpose sequential computing devices becomes challenging. Field Programmable Gate Array (FPGA) has demonstrated to be an efficient tool for the acceleration of computationally challenging applications by utilizing parallelism in the computations. In this study, an FPGA-based hardware accelerator is exploited to propose a fast and robust fingerprint identification solution that is based on a generalized minutiae neighbor based encoding and matching algorithm. The proposed FPGA implementation employs the Distributed RAM resources efficiently by using them as look-up tables for matching the encoded minutiae features. The proposed FPGA-based fingerprint matching system has the potential to match 2.75 million fingerprints per second while maintaining a low error rate. The proposed system can be deemed as an effective solution for Automated Fingerprint Identification Systems (AFIS) for large-scale applications.
•A generalized minutiae-neighbor based fingerprint encoding and matching algorithm is mapped on FPGA technology.•FPGA Distributed–RAM resources are efficiently exploited as look-up tables for comparing the encoded minutiae.•The proposed FPGA based fingerprint matching system has the potential to match 2.75 million fingerprints per second while maintaining a low error rate.