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

IEEE Transactions on Biometrics, Behavior, and Identity Science. 2025. Vol. 7. No. 4. P. 668–680.

Khodadoust J., Monroy R., Marasco E.

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.

Keywords: Fingerprint recognition, отпечатки пальцев