FingerSTR: Weak Supervised Transformer for Latent Fingerprint Segmentation.

Latent fingerprint segmentation is a crucial process in contemporary biometric systems utilized in criminal investigations and security applications. Accurately segmenting the fingerprint region from the background noise and artifacts, which can be challenging due to the complexity of the surrounding environment, is the primary goal of this process. Although various methodologies, including binarization-based, texture-based, and deep learning-based segmentation approaches have been proposed, they are often limited by environmental noise and a scarcity of annotated data, resulting in a low segmentation accuracy rate. In this paper, we propose FingerSTR (Finger Segmentation Transformer), a fully Transformer-based latent fingerprint segmentation network, and introduce a new teacher-student training methodology to achieve more precise and robust segmentation results without requiring manual annotation. Based on experimental results of latent fingerprint database NIST SD27, FingerSTR surpasses both deep-learning algorithms and handcraft methods, achieving state-of-the-art performance in the latent fingerprint segmentation task.