Researchers at the UK’s University of Manchester have collaborated with the Universidad Autónoma de Madrid in Spain to develop an artificial intelligence (AI)-based biometric verification system for airport security.
The system is capable of measuring a person’s individual gait or walking pattern and can verify footsteps of an individual simply by walking on a pressure pad in the floor and analysing the footstep 3D and time-based data.
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Based on findings of the study, it was found that the AI-based system correctly identified an individual almost 100% of the time, with just a 0.7 error rate.
With the new discovery, instead of fingerprinting and eye-scanning, behavioural biometrics such as gait recognition, could be used as a biometric at airport security.
Manchester School of Electrical and Electronic Engineering researcher Dr Omar Costilla Reyes said: “Each human has approximately 24 different factors and movements when walking, resulting in every individual person having a unique, singular walking pattern.
“Therefore monitoring these movements can be used, like a fingerprint or retinal scan, to recognise and clearly identify or verify an individual.”
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By GlobalDataThe team collected the largest footstep database using floor-only sensors and high-resolution cameras, which includes about 20,000 footstep signals from 127 different individuals, to accumulate the samples and dataset.
Dr Costilla Reyes added: “Focusing on non-intrusive gait recognition by monitoring the force exerted on the floor during a footstep is very challenging.
“That’s because distinguishing between the subtle variations from person to person is extremely difficult to define manually, that is why we had to come up with a novel AI system to solve this challenge from a new perspective.”
Contrary to being filmed or scanned at an airport, the process is non-intrusive for the individual and resilient to noisy environments.
Gait measurements ensure that the person does not need to remove footwear when walking on the pressure pads as it is not based on the footprint shape.
