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Updated: May 10, 2025

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation

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TipSegNet: Fingertip Segmentation in Contactless Fingerprint Imaging.

Laurenz Ruzicka^1,2, Bernhard Kohn², Clemens Heitzinger³

¹Faculty of Physics, TU Wien, 1040 Vienna, Austria.

Sensors (Basel, Switzerland)

|April 28, 2025

Summary

This summary is machine-generated.

TipSegNet accurately segments fingertips from hand images for hygienic contactless fingerprint recognition. This deep learning model significantly improves biometric system reliability by achieving near-perfect accuracy in challenging conditions.

Keywords:

biometrics contactless fingerprint segmentation

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Last Updated: May 10, 2025

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Area of Science:

Computer Science
Biometrics
Image Processing

Background:

Contactless fingerprint recognition offers advantages over traditional methods but requires precise fingertip segmentation.
Accurate segmentation is challenging due to varying finger poses and background conditions.

Purpose of the Study:

To introduce TipSegNet, a novel deep learning model for accurate fingertip segmentation in contactless biometrics.
To enhance the performance and robustness of contactless fingerprint recognition systems.

Main Methods:

Developed TipSegNet, a deep learning model utilizing a ResNeXt-101 backbone and Feature Pyramid Network (FPN).
Employed extensive data augmentation for improved generalizability.
Trained and evaluated the model on a dataset of 2257 labeled hand images.

Main Results:

TipSegNet achieved state-of-the-art performance in fingertip segmentation.
The model attained a mean intersection over union (mIoU) of 0.987 and an accuracy of 0.999.
Demonstrated superior performance compared to existing methods.

Conclusions:

TipSegNet represents a significant advancement in contactless fingerprint segmentation.
The model's high accuracy can substantially improve the reliability of real-world contactless biometric systems.