Iterative Framework For Data Augmentation Of Segmented Fingerprints
Abstract
Infant biometrics presents unique challenges due to the physiological differences between infants and adults, compounded by the scarcity of available data for research that limits the development of robust matching systems. This paper proposes a novel data augmentation method that uses iterative techniques to generate diverse variants of segmented fingerprints by inducing errors in a convolutional neural network trained to extract fingerprint ridges and valleys. Experiments on real infant fingerprints demonstrate the method's effectiveness in expanding fingerprint variability, with augmentations exhibiting significant fluctuations in minutiae counts while still retaining visual similarity to the originals. The study also highlights the method's customizable nature for applying varying levels of changes to fingerprint segmentations. Future research includes training segmentation and matching neural networks using datasets augmented by the proposed framework.
Keywords:
Augmentation, Data, Fingerprint, Infant, Segmentation
References
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N.-T. Tran, V.-H. Tran, N.-B. Nguyen, T.-K. Nguyen, and N.-M. Cheung, “On data augmentation for GAN training,” IEEE Transactions on Image Processing, vol. 30, pp. 1882–1897, 2021. DOI: 10.1109/TIP.2021.3058436
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A. Farina, Z. M. Kovács-Vajna, and A. Leone, “Fingerprint minutiae extraction from skeletonized binary images,” Pattern Recognition, vol. 32, no. 5, pp. 877–889, 1999. [Online]. Available: [link] DOI: 10.1016/S0031-3203(98)00107-1
L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology, vol. 26, no. 3, pp. 297–302, 1945. DOI: 10.2307/1932409
T. Sørensen, A Method of Establishing Groups of Equal Amplitude in Plant Sociology Based on Similarity of Species Content and Its Application to Analyses of the Vegetation on Danish Commons, ser. Biologiske skrifter. Munksgaard in Komm., 1948.
E. Rodrigues, T. Porcino, A. Conci, and A. Silva, “A simple approach for biometrics: Finger-knuckle prints recognition based on a sobel filter and similarity measures,” International Conference on Systems, Signals and Image Processing (IWSSIP), 2016.
M. Liu and P. Qian, “Automatic segmentation and enhancement of latent fingerprints using deep nested UNets,” IEEE Transactions on Information Forensics and Security, vol. 16, pp. 1709–1719, 2021.
Published
2024-11-06
How to Cite
DALL AGNOL, João Leonardo Harres; BONA, Wesley Augusto de; RODRIGUES, Érick Oliveira; SOUTHIER, Luiz Fernando Puttow; OLIVA, Jefferson; FILIPAK, Marcelo; CASANOVA, Dalcimar.
Iterative Framework For Data Augmentation Of Segmented Fingerprints. In: WORKSHOP ON INFORMATION SYSTEMS (WSIS), 15. , 2024, Rio Paranaíba/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 15-20.
DOI: https://doi.org/10.5753/wsis.2024.33666.
