Deep Learning-Based Semantic Segmentation for Touchless Fingerprint Recognition

摘要

Fingerprint recognition is one of the most popular biometric technologies. Touchless fingerprint systems do not require contact of the finger with the surface of a capture device. For this reason, they provide an increased level of hygiene, usability, and user acceptance compared to touch-based capturing technologies. Most processing steps of the recognition workflow of touchless recognition systems differ in comparison to touch-based biometric techniques. Especially the segmentation of the fingerprint areas in a 2D capturing process is a crucial and more challenging task. In this work a proposal of a fingertip segmentation using deep learning techniques is presented. The proposed system allows to submit the segmented fingertip areas from a finger image directly to the processing pipeline. To this end, we adapt the deep learning model DeepLabv3+ to the requirements of fingertip segmentation and trained it on the database for hand gesture recognition (HGR) by extending it with a fingertip ground truth. Our system is benchmarked against a well-established color-based baseline approach and shows more accurate hand segmentation results especially on challenging images. Further, the segmentation performance on fingertips is evaluated in detail. The gestures provided in the database are separated into three categories by their relevance for the use case of touchless fingerprint recognition. The segmentation performance in terms of Intersection over Union (IoU) of up to 68.03% on the fingertips (overall: 86.13%) in the most relevant category confirms the soundness of the presented approach.