Abstract
Fingerprint recognition remains one of the most reliable biometric technologies due to its high accuracy and uniqueness. Traditional systems rely on contact-based scanners, which are prone to issues such as image degradation from surface contamination and inconsistent user interaction. To address these limitations, contactless fingerprint recognition has emerged as a promising alternative, providing non-intrusive and hygienic authentication. This study evaluates the impact of image enhancement tech-niques on the performance of pre-trained deep learning models using transfer learning for touchless fingerprint recognition. The IIT-Bombay Touchless and Touch-Based Fingerprint Database, containing data from 200 subjects, was employed to test the per-formance of deep learning architectures such as VGG-16, VGG-19, Inception-V3, and ResNet-50. Experimental results reveal that transfer learning methods with fingerprint image enhance-ment (indirect method) significantly outperform those without enhancement (direct method). Specifically, VGG-16 achieved an accuracy of 98% in training and 93% in testing when using the enhanced images, demonstrating superior performance compared to the direct method. This paper provides a detailed comparison of the effectiveness of image enhancement in improving the accuracy of transfer learning models for touchless fingerprint recognition, offering key insights for developing more efficient biometric systems.
Abstract (translated)
指纹识别由于其高准确性和独特性，仍然是最可靠的生物识别技术之一。传统的系统依赖于接触式扫描仪，这种设备容易受到表面污染导致图像退化以及用户互动不一致等问题的影响。为了解决这些问题，非接触式指纹识别作为一种有前途的替代方案应运而生，它提供了无侵犯且卫生的身份认证方式。本研究评估了图像增强技术对使用迁移学习预训练深度学习模型进行非接触式指纹识别性能的影响。实验中采用IIT-孟买非接触和接触式指纹数据库，该数据库包含200名受试者的数据，并测试了VGG-16、VGG-19、Inception-V3 和 ResNet-50等深度学习架构的性能。 实验证明，使用指纹图像增强技术（间接方法）的迁移学习方法显著优于未进行增强处理的方法（直接方法）。具体而言，在使用增强后的图像时，VGG-16模型在训练中达到了98%的准确率，在测试中的准确率为93%，这表明其性能明显优于直接方法。本文详细比较了图像增强技术在提升非接触式指纹识别迁移学习模型准确性方面的有效性，并为开发更高效的生物识别系统提供了关键见解。
URL
https://arxiv.org/abs/2502.04680
