English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper proposes a method for utilizing thermal features of the hand forthe purpose of presentation attack detection (PAD) that can be employed in ahand biometrics system's pipeline. By envisaging two different operationalmodes of our system, and by employing a DCNN-based classifiers fine-tuned witha dataset of real and fake hand representations captured in both visible andther- mal spectrum, we were able to bring two important deliverables. First, aPAD method operating in an open-set mode, capable of correctly discerning 100%of fake thermal samples, achieving Attack Presentation Classification ErrorRate (APCER) and Bona-Fide Presentation Classification Error Rate (BPCER) equalto 0%, which can be easily implemented into any existing system as a separatecomponent. Second, a hand biometrics system operating in a closed-set mode,that has PAD built right into the recognition pipeline, and operatingsimultaneously with the user-wise classification, achieving rank-1 recognitionaccuracy 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 classificationaccuracy of a biometric system, when coupled with visible light images. Tofollow the reproducibility guidelines and to stimulate further research in thisarea, we share the trained model weights, source codes, and a newly createddataset of fake hand representations with interested researchers.

Thermal Features for Presentation Attack Detection in Hand Biometrics