Human Facial Understanding & Anti-Spoofing using Neural Networks on Time of Flight Cameras

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Human Facial Understanding & Anti-Spoofing using Neural Networks on Time of Flight Cameras

Event: AutoSens Detroit

, 2022

Hear from:

Vish Rajalingam

Lead AI Solutions Architect,

MultiCoreWare

Vish Rajalingam

Lead AI Solutions Architect,

MultiCoreWare

Deep learning advancements have shown significant promise in monitoring driver habits and actions. A robust Driver Monitoring Alert system is an essential component of Euro NCAP regulations, and the most widely adopted systems today are RGB Camera-based. RGB cameras demonstrate a great promise in modelling driver behaviour, but they have issues with illumination changes, occlusions, and anti-spoofing.

As AI technology advances, so does sensor technology, with indirect Time of Flight cameras being a prominent example (iToF). A iToF sensor can provide 2D amplitude images and distance images, giving it the advantage of being resilient to ambient lighting and providing additional depth information.

MulticoreWare will present our findings from using neural networks on Melexis iToF sensors and demonstrate their efficiency for modules such as Face Detection and Face Recognition that enable applications like Driver Authentication, Drowsiness Detection, etc. Furthermore, we will illustrate iToF cameras’ extensive ability to detect Anti-Spoofing (Print Attack) leveraging the distance images.