State-of-the-art (SOTA) models typically use large datasets for pre-training and then fine-tune on smaller datasets for better performance. However, the high computational cost can be a barrier for many researchers. There is a need to focus on data size-independent models suited for data-scarce scenarios, which is essential for tasks like fingerprint recognition and could make research more accessible and generalizable in resource-limited environments. With this aim, this paper presents a novel approach to the difficulties in contactless fingerprint recognition, particularly with scarce and poor-quality challenging dataset images due to contactless acquisition. Our proposed system uses a 'Scattering using a Shearlet Network (SSNet)' to extract fingerprint features and a score-level fusion scheme to improve authentication accuracy. In contrast to the computationally expensive and mathematically less transparent dense deep learning networks such as vision transformers, attention networks, deep learning-based hybrid approaches, etc., SSNet is an economical framework with fixed filters. The SSNet is a replacement to the Scattering Wavelet Network (SWN) that utilizes a Complex Morlet Wavelet (CMW). Our model significantly improves verification and identification accuracy over SOTA approaches, particularly with scarce and poor-quality challenging datasets.
