Abstract
In this study, we propose a novel method for detecting minutiae in fingerprint images using YOLOv5, a state-of-the-art object detection algorithm. Our approach utilizes a convolutional neural network (CNN) to identify and locate minutiae points, such as ridge endings and bifurcations, within a fingerprint image. We trained the CNN on a dataset of fingerprint images and corresponding minutiae annotations, and evaluated its performance using standard metrics such as precision, recall, mAP 0.5 and mAP 0.5:0.95. Our results indicate that the proposed method is able to accurately detect minutiae in fingerprint images with high precision - 91%, recall - 82%, mAP 0.5 - 89% and mAP 0.5:0.95 - 39%. Furthermore, we demonstrate that the YOLOv5-based approach is significantly faster than traditional minutiae detection methods, making it suitable for real-time applications. In conclusion, this study presents a promising approach for the automated detection of minutiae in fingerprint images using YOLOv5.
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References
Result of algorithm presented in paper
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Results of database image after Gabor enhancement in proposed algorithm
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Acknowledgments
The work was supported by grant no. WZ//I-IIT/5/2023 from Bialystok University of Technology and funded with resources for research by the Ministry of Education and Science in Poland.
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Trusiak, K., Saeed, K. (2023). Finger Minutiae Extraction Based on the Use of YOLO-V5. In: Saeed, K., Dvorský, J., Nishiuchi, N., Fukumoto, M. (eds) Computer Information Systems and Industrial Management. CISIM 2023. Lecture Notes in Computer Science, vol 14164. Springer, Cham. https://doi.org/10.1007/978-3-031-42823-4_4
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DOI: https://doi.org/10.1007/978-3-031-42823-4_4
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