Scientists have developed an artificial intelligence (AI) that can identify people by measuring their gait or walking pattern. The technology could be used at airport security instead of fingerprinting and eye-scanning. It can successfully verify an individual simply by analyzing the footstep 3D and time-based data.
The AI system, developed by researchers at the University of Manchester in the UK and the University of Madrid in Spain, correctly identified an individual almost 100 percent of the time, with just a 0.7 error rate. Physical biometrics, such as fingerprints, facial recognition, and retinal scans, is currently more commonly used for security purposes. However, so-called behavioral biometrics, such as gait recognition, also capture unique signatures delivered by a person's natural behavioral and movement patterns.
The team tested their data by using a large number of so-called 'impostors' and a small number of users in three different real-world security scenarios. These were airport security checkpoints, the workplace, and the home environment. The approach offers the potential to complement current security systems at airports, offices and the home as the research has shown.
"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 recognize and clearly identify or verify an individual," said Omar Costilla Reyes, from the University of Manchester. To create the AI system that computers need to learn such movements patterns, the team collected the largest footstep database in history, containing nearly 20,000 footstep signals from 127 different individuals.
To compile the samples and dataset the team used floor-only sensors and high-resolution cameras. This dataset, called SfootBD, was used to develop the advanced computational models needed for automatic footprint biometric verification presented in TPAMI. "Focussing on non-intrusive gait recognition by monitoring the force exerted on the floor during a footstep is very challenging," said Costilla Reyes.
"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," he said.
One the key benefits of using footprint recognition is, unlike being filmed or scanned at an airport, the process is non-intrusive for the individual and resilient to noise environmental conditions. The person does not even need to remove their footwear when walking on the pressure pads as it isn't based on the footprint shape itself but with their gait. Other applications for the technology include smart steps that could recognise neurodegeneration which could have positive implications in the healthcare sector. This is another area that Costilla Reyes intends to advance his research with footstep recognition.