Evaluating Contactless Fingerprint Segmentation for Interoperable Biometric Identification Systems

Fingerprint recognition is a popular and cost-effective biometric technology. Most existing systems require specialized hardware to capture fingerprints, but contactless fingerprint capture can be performed using smartphone images. This method reduces costs and enhances hygiene and usability. A new image processing workflow is needed to facilitate contactless fingerprint capture, with segmentation a critical step. This study explores the feasibility of using smartphone images for this purpose. It evaluates four deep learning models - SSD MobileNetV2, SSD MobileNetV2 FPN Lite, Mask R-CNN, and U-Net - using the ISPFDv2 dataset. Results show that Mask R-CNN performed best in segmenting fingertip regions, while SSD MobileNetV2 had the highest recognition accuracy against traditional fingerprint databases. These findings demonstrate the potential of deep learning for effective contactless fingerprint recognition using smartphones.