VerifNet - A Novel Score Fusion-Based Method Leveraging Wavelets With Deep Learning and Minutiae Matching for Contactless Fingerprint Recognition

This paper introduces a novel approach to a complete contactless biometric system that takes a finger photo image as an input and performs various image processing techniques and authenticates the fingerprints for an easy, non-invasive system that is efficient and robust. While contact-based fingerprint recognition systems have produced ground-breaking achievements, these systems face issues with latent fingerprints, sensor degradation brought on by frequent physical touch, and hygiene issues. Thus, the next step towards solving these issues is developing a contactless system that counters all the mentioned issues as faced by a contact-based fingerprint recognition system. This paper introduces a novel deep learning architecture that fuses the scattering wavelet transform making it lightweight and computationally efficient. A unique combination of the Siamese network integrated with wavelets and the traditional minutiae-based approach builds the core framework for the recognition system. The combination of these techniques allows the system to perform fingerprint recognition with high accuracy. This approach performs with an Equal Error Rate (EER) of 2.5% on the IITI-CFD, 2.5% on the PolyU 2D Contactless Dataset, and 3.76% on the IITB Touchless Fingerprint Dataset. Through, this paper, we aim to develop a biometric system that achieves a balance between economy and efficiency.