Pith. sign in

REVIEW

Behavioural Curves Analysis Using Near-Infrared-Iris Image Sequences

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2203.02488 v1 pith:2DGXRRHA submitted 2022-03-04 cs.CV cs.LG

L. Causa (1) , J. E. Tapia (2 , 3) , E. Lopez-Droguett (4) , A. Valenzuela (2) , D. Benalcazar (2) , C. Busch (3) ((1) TOC Biometrics , Research
show 12 more authors
Development Centre Chile. (2) Universidad de Chile DIMEC Chile. (3) da/sec-Biometrics Internet Security Research Group Hochschule Darmstadt Germany. (4) Department of Civil Environmental Engineering Garrick Institute for the Risk Sciences University ofCalifornia Los Angeles USA)
This is my paper · ORCID
classification cs.CVcs.LG
keywords irisbehaviourresultsalcoholanalysisbehaviouralbestcapture
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper proposes a new method to estimate behavioural curves from a stream of Near-Infra-Red (NIR) iris video frames. This method can be used in a Fitness For Duty system (FFD). The research focuses on determining the effect of external factors such as alcohol, drugs, and sleepiness on the Central Nervous System (CNS). The aim is to analyse how this behaviour is represented on iris and pupil movements and if it is possible to capture these changes with a standard NIR camera. The behaviour analysis showed essential differences in pupil and iris behaviour to classify the workers in "Fit" or "Unfit" conditions. The best results can distinguish subjects robustly under alcohol, drug consumption, and sleep conditions. The Multi-Layer-Perceptron and Gradient Boosted Machine reached the best results in all groups with an overall accuracy for Fit and Unfit classes of 74.0% and 75.5%, respectively. These results open a new application for iris capture devices.

Discussion (0). Continue with ORCID to comment.

Pith tools