Abstract
Fingerprint recognition is now a common, well known and generally accepted form of biometric authentication. The popularity of fingerprint recognition also makes it the focus of many studies which aim to constantly improve the technology in terms of factors such as accuracy and speed. This study sets out to create fingerprint recognition architectures which improve upon pre-trained architectures - named ConDense - that provide stronger if not comparable accuracy in comparison to related works on the authentication/identification task. Each of these ConDense architectures are tested against databases 1A, 2A, 3A provided by FVC 2006. The ConDense architectures presented in this study performed well across the varying image qualities in the given databases, with the lowest EERs achieved by this study’s architectures being 1.385% (DB1A), 0.041% (DB2A) and 0.871% (DB3A). In comparison to related works, the architectures presented in this study performed the best in terms of EER against DB1A, and DB3A. The lowest EER for DB2A reported by a related work was 0.00%.
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Lang, D., van der Haar, D. (2022). ConDense: Multiple Additional Dense Layers with Fine-Grained Fully-Connected Layer Optimisation for Fingerprint Recognition. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13364. Springer, Cham. https://doi.org/10.1007/978-3-031-09282-4_2
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DOI: https://doi.org/10.1007/978-3-031-09282-4_2
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