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How 3D Sensing Enables Mobile Face Recognition

3D sensing is the optical technology behind mobile face recognition – and it depends on innovative optical components.

1 min read
Ams

The popularity of the face recognition function in the latest high-end smartphones has brought the spotlight on to the 3D optical sensing technology which enables it. Three techniques for 3D sensing can be used to implement face recognition, and all are supported by advanced optical components and technologies supplied by ams.

AMS

Depth map: the basis of 3D face recognition

In mobile face recognition, a depth map captured by the phone’s 3D sensors is compared to a reference 3D image of the user. This 3D depth map generates more data about the face than a conventional 2D camera’s image. Secure 3D authentication enables the use of face recognition in critical applications such as mobile payments.

The techniques for generating a facial depth map include:

  • Time-of-flight sensing – measures distance by timing the flight of infrared light from the emitter to the user’s face and back to a photosensor.

  • Stereo imaging – as in human vision, two spaced photosensors create perspective and depth. Infrared light projectors enable an Active Stereo Vision system to work with no ambient light.

  • Structured light – algorithms generate depth maps by analyzing the distortions in random patterns of dots projected on the user’s face.

    Learn more about the ams innovations in 3D sensing which are enabling smartphone manufacturers’ face recognition design programs, including eye-safe VCSEL laser emitter arrays, time-of-flight sensors, and system software developed in partnership with technology developers such as Face++.

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Self-Driving Cars Work Better With Smart Roads

Intelligent infrastructure makes autonomous driving safer and less expensive

9 min read
A photograph shows a single car headed toward the viewer on the rightmost lane of a three-lane road that is bounded by grassy parkways, one side of which is planted with trees. In the foreground a black vertical pole is topped by a crossbeam bearing various instruments. 

This test unit, in a suburb of Shanghai, detects and tracks traffic merging from a side road onto a major road, using a camera, a lidar, a radar, a communication unit, and a computer.

Shaoshan Liu

Enormous efforts have been made in the past two decades to create a car that can use sensors and artificial intelligence to model its environment and plot a safe driving path. Yet even today the technology works well only in areas like campuses, which have limited roads to map and minimal traffic to master. It still can’t manage busy, unfamiliar, or unpredictable roads. For now, at least, there is only so much sensory power and intelligence that can go into a car.

To solve this problem, we must turn it around: We must put more of the smarts into the infrastructure—we must make the road smart.

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