Book Title: Contemporary Research Across Disciplines
Editors: Dr. R. Saravana Selvakumar and Mr. R. Venkatesan
ISBN: 978-81-978738-1-2
Chapter: 25
DOI: https://doi.org/10.59646/crc25/278
Author: Mrs. V. Subathra, Assistant Professor, Department of Information Technology, G. Venkataswamy Naidu College (Autonomous), Kovilpatti, Tamil Nadu, India.
Abstract
The fingerprint security technology has recently garnered significant attention due to its unique biometric characteristics, which remain unchanged throughout an individual’s lifetime, making it a highly secure and reliable means of personal identification. The Korean police employ an Automated Fingerprint Identification System (AFIS) to ascertain the identity of individuals. In this study, we developed a deep learning system to categorize fingerprints based on their hand origin—left or right—using advanced convolutional neural network techniques. We experimented with several prominent deep learning models, such as Classic CNN, AlexNet, ResNet50, VGG-16, and YOLO, all well-recognized for their efficacy in image analysis tasks. The training phase involved 9,080 fingerprint images, while 1,000 images were set aside for evaluating the model’s accuracy. Our results revealed that the ResNet50 model achieved a % classification accuracy of 96.80%, outperforming the other models in distinguishing between left and right-hand fingerprints.
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