Year: 2023
Type: conference-paper
Abstract: With the advancement of digital systems, biometric authentication methods, especially fingerprint recognition, have become an integral component of various security protocols. However, these systems remain susceptible to spoofing attacks where counterfeit fingerprints can be used maliciously. This research aims to address the challenge of fingerprint liveness detection by leveraging the capabilities of Convolutional Neural Networks (CNNs). Using the SocoFing dataset, we designed and implemented a CNN-based model, highlighting its architecture comprising multiple convolutional layers, pooling layers, and dense layers. The model was trained with the Adam optimizer and evaluated using metrics such as accuracy, False Acceptance Rate (FAR), and False Rejection Rate (FRR). Our results offer promising insights into the robustness of CNNs in detecting genuine versus spoofed fingerprints. Furthermore, this study discusses challenges faced during implementation, implications for real-world applications, and potential avenues for future research in the realm of biometric security.
Authors: Yurii Myshkovskyi, Mariia Nazarkevych
Institution: Lviv Polytechnic National University
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7166319771