A Unified Framework for Segmentation, Scaling, and Indexing of Contactless Fingerprints

Contactless fingerprint identification is gaining prominence due to its convenience, accessibility, security, and hygiene benefits over traditional contact-based methods. However, achieving accurate identification and efficiently searching large-scale databases remain challenging. In this paper, we introduce CFinSegNet, an encoder-decoder network for fingerprint segmentation that operates on preprocessed fingerprints and multiple image representations from diverse color spaces rather than the original fingerprint images. Additionally, we present FreqFinScale, a no-reference regression-based model designed to handle scaling variations using frequency representations. Furthermore, we propose FreqFinIndex, a deep neural network architecture for indexing and retrieving contactless fingerprints, which compresses high-dimensional frequency domain data into a compact latent space for fast and accurate search. Our experimental evaluation on five publicly available datasets demonstrates that our method significantly outperforms state-of-the-art approaches in accuracy and efficiency.