FingerUNeSt++: Improving Fingertip Segmentation in Contactless Fingerprint Imaging Using Deep Learning

Biometric identification systems, particularly those utilizing fingerprints, have become essential as a means of authenticating users due to their reliability and uniqueness. The recent shift towards contactless fingerprint sensors requires precise fingertip segmentation with changing backgrounds, to maintain high accuracy. This study introduces a novel deep learning model combining ResNeSt and UNet++ architectures called FingerUNeSt++, aimed at improving segmentation accuracy and inference speed for contactless fingerprint images. Our model significantly outperforms traditional and state-of-the-art methods, achieving superior performance metrics. Extensive data augmentation and an optimized model architecture contribute to its robustness and efficiency. This advancement holds promise for enhancing the effectiveness of contactless biometric systems in diverse real-world applications.