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Journals / Electrica / 2021 / Cilt: 21 - Sayı: 3

Minutiae-Based Fingerprint Identification Using Gabor Wavelets and CNN Architecture

Pelin GÖRGEL
Abdulsamet EKŞİ
Journal
Electrica
Pages
480–490
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results demonstrate that the classification accuracy is 91.50% and the proposed approach can achieve good results even with poor quality images

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Fingerprint identification is still a challenging issue for confident authentication.
📊
Key Findings & Data:Both Gabor wavelet and minutiae features, such as ridge bifurcation and ending enhancement, represent the significant information belonging to fingerprint images.
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Conclusion & Impact:Experimental results demonstrate that the classification accuracy is 91.50% and the proposed approach can achieve good results even with poor quality images
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

Fingerprint identification is still a challenging issue for confident authentication. In this study, we present a methodology that comprises pre-processing, minutiae detection, and Gabor wavelet transform. Both Gabor wavelet and minutiae features, such as ridge bifurcation and ending enhancement, represent the significant information belonging to fingerprint images. Pre-processing algorithm affects minutiae extraction performance. So we use the dilation morphological operation and thinning for the enhancement. Then Gabor wavelet transform is applied to minutiae-extracted images to increase the identification performance. The classification problem is solved using a proper convolutional neural network (CNN) with a three-layer convolutional model and appropriate filter sizes. Experimental results demonstrate that the classification accuracy is 91.50% and the proposed approach can achieve good results even with poor quality images

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Cite This Article

GÖRGEL, P., EKŞİ, A. (2021). Minutiae-Based Fingerprint Identification Using Gabor Wavelets and CNN Architecture. Electrica, Cilt: 21 - Sayı: 3, 480–490. https://ajindex.com/articles/393424

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