TipSegNet: Fingertip Segmentation in Contactless Fingerprint Imaging
Laurenz Ruzicka, Bernhard Kohn, Clemens Heitzinger

TL;DR
TipSegNet is a deep learning model that significantly improves fingertip segmentation accuracy in contactless fingerprint imaging, enhancing biometric system reliability.
Contribution
The paper introduces TipSegNet, a novel deep learning approach combining ResNeXt-101 and FPN for robust fingertip segmentation under challenging conditions.
Findings
Achieves a mean IoU of 0.987 in fingertip segmentation
Outperforms existing segmentation methods in accuracy and robustness
Demonstrates high generalizability with extensive data augmentation
Abstract
Contactless fingerprint recognition systems offer a hygienic, user-friendly, and efficient alternative to traditional contact-based methods. However, their accuracy heavily relies on precise fingertip detection and segmentation, particularly under challenging background conditions. This paper introduces TipSegNet, a novel deep learning model that achieves state-of-the-art performance in segmenting fingertips directly from grayscale hand images. TipSegNet leverages a ResNeXt-101 backbone for robust feature extraction, combined with a Feature Pyramid Network (FPN) for multi-scale representation, enabling accurate segmentation across varying finger poses and image qualities. Furthermore, we employ an extensive data augmentation strategy to enhance the model's generalizability and robustness. TipSegNet outperforms existing methods, achieving a mean Intersection over Union (mIoU) of 0.987…
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Taxonomy
TopicsBiometric Identification and Security · Forensic Fingerprint Detection Methods
