This AI Knows Who You Are by the Way You Walk

footstep-recognition using Artificial Intelligence

We have individuality even in our walking and is the basis of the new technique called footstep-recognition using Artificial Intelligence (AI). Soon this technique will take over the retinal scanners and fingerprinting at security checkpoints, including airports.

It is possible to recognise a person’s body language and gait by neural networks .This in turn can be used to recognize and identify them with almost perfect accuracy,

The new SfootBD system is nearly 380 times more accurate than earlier methods, and it doesn’t require a person to go barefoot in order to work. It’s less invasive than other behavioural biometric verification systems and could be used covertly.

Each human has approximately 24 different factors and movements when walking, resulting in every individual person having a unique, singular walking pattern. With a largest database of over 20,000 footstep signals from more than 120 individuals each gait was measured using pressure pads on the floor and a high-resolution camera. An artificially intelligent system called a deep residual neural network scoured through the data, analysing aspects of the gait, rather than the shape of the footprint

  • Weight distribution,
  • Gait speed, and
  • Three-dimensional measures of each walking style.

Earlier attempts at footstep recognition involved the scanning of individuals without their shoes on and a 3D-imaging technique that compared a person’s walking style to CCTV footage. The new technique is more accurate than both, though it does require the use of special floor pads.

To test the SfootBD system, Reyes’ team monitored participants in

Three typical scenarios were chosen to test SfootBD

  • Airport security checkpoints,
  • Workplaces, and
  • Homes

There searchers also tested a control group of imposters to see if the AI could tell when someone was trying to fake another person’s gait (which it could). Results showed that, on average, the system was 100 percent accurate in identifying individuals, with an error rate of just 0.7 percent.


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