# Thermal Features for Presentation Attack Detection in Hand Biometrics

arXiv: https://arxiv.org/abs/1809.04364 · HTML: https://mlsanity.com/paper/1809.04364/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [Spoofing Detection on Hand Images Using Quality Assessment](https://mlsanity.com/paper/2110.12923/) (2021)
- [Two-Stage Human Verification using HandCAPTCHA and Anti-Spoofed Finger Biometrics with Feature Selection](https://mlsanity.com/paper/2410.09866/) (2024)

## Abstract (arXiv)

This paper proposes a method for utilizing thermal features of the hand for the purpose of presentation attack detection (PAD) that can be employed in a hand biometrics system's pipeline. By envisaging two different operational modes of our system, and by employing a DCNN-based classifiers fine-tuned with a dataset of real and fake hand representations captured in both visible and ther- mal spectrum, we were able to bring two important deliverables. First, a PAD method operating in an open-set mode, capable of correctly discerning 100% of fake thermal samples, achieving Attack Presentation Classification Error Rate (APCER) and Bona-Fide Presentation Classification Error Rate (BPCER) equal to 0%, which can be easily implemented into any existing system as a separate component. Second, a hand biometrics system operating in a closed-set mode, that has PAD built right into the recognition pipeline, and operating simultaneously with the user-wise classification, achieving rank-1 recognition accuracy of up to 99.75%. We also show that thermal images of the human hand, in addition to liveness features they carry, can also improve classification accuracy of a biometric system, when coupled with visible light images. To follow the reproducibility guidelines and to stimulate further research in this area, we share the trained model weights, source codes, and a newly created dataset of fake hand representations with interested researchers.