Performance Evaluation of Image Enhancement Techniques on Transfer Learning for Touchless Fingerprint Recognition
S. Sreehari, P D Dilavar, S. M. Anzar, Alavikunhu Panthakkan, Saad Ali Amin · 2024
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 techniques 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 performance of deep learning architectures such as VGG-16, VGG19, Inception-V3, and ResNet-50. Experimental results reveal that transfer learning methods with fingerprint image enhancement (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.