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As a global leader in innovative light and sensor solutions we offer a unique product and technology portfolio for sensing, illumination and visualization:
from high-performance LEDs and lasers to mixed-signal analog ICs and sensors.
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3D Vision System

3D Vision System

Flood NIR: near-infrared image, acquired with assist of a PMSILPlus flood illuminator. These images are used for 2D image analysis that will be fused with the 3D image data.

> More about  flood illuminator Merano-Hybrid 
> More about  flood illuminator Merano-PD


Dot NIR: near-infrared image, acquired with assist of a dot projector. The dot pattern is enhancing contrast in the image to provide a very good depth map in all lighting conditions with low contrast objects in the scene.

Depth Map: ams face recognition use depth map information of the user’s face. A facial depth map is a set of thousands of coordinates mapped in three-dimensional space describing the contours of the surface of the user’s head relative to a single point of view in front of the user. This depth map may be compared with a reference depth map of the user’s face to authenticate the user. 

Reconstructed 3D image is handed over to the application software for further use case specific processing (e.g. face recognition).

3D dToF for Mobile Augmented Reality (AR)

3D dToF for Mobile AR

ams provides a complete technology stack – from optical sensing through to scene reconstruction and integration with RGB camera – for world-facing 3D direct Time-of-Flight sensing in mobile devices. This technology for world-facing mobile aims to achieve higher range and lower power consumption than other implementations.

Integrating ams’ 3D optical sensing solutions and a partner’s advanced middleware and software for simultaneous localization and mapping (SLAM) and 3D image processing offers the option for manufacturers to quickly and more simply implement augmented reality (AR) functions on mobile devices. The high-performance, low-power dToF sensing system also supports 3D environment and object scanning, camera image enhancement, and camera auto-focus assistance in dark conditions.

Combining best-in-class technologies, the new 3D dToF system includes high-power infrared VCSEL array, dot-pattern optical system, and a high sensitivity sensor. Read also our press release about ams 3D dToF sensor solution.

3d dToF augmented reality for mobile
Face Recognition

Face Recognition

Face recognition is widely known as a technology used to unlock smartphones and support authentication in applications such as mobile payments. It is also deployed at border controls within automated passport control machines.

Face Recognition Biometry Engine (FRBE) technology is the result of 10 years of biometric face recognition and machine learning R&D efforts. ams 3D Face Recognition Solutions are based on mature and optimized algorithms.  Our in-house developed face recognition software can be rapidly adopted to OEMs needs in case further enhancements are required.

Reference Designs

  • Active Stereo Vision 
  • ToF 
Active Stereo Vision

Active Stereo Vision

Stereo matching algorithms might fail to find correspondences in surface with uniform color, low texture and no features. Passive stereo vision systems rely on the ambient light to capture images and perform poorly under low light conditions. For optimal performance, active stereo vision includes an additional pattern light projector, such as the ams Belago dot projector, to enhance feature extraction and low light performance. The illumination increase the light intensity and a number of features in the scene to overcome typical system limitations.

> More about our latest dot projector Belago

> More about our latest flood illuminator PMSILPlus

> Watch our demo video about 3D face recognition
 

ams projector graphic
2-Step Enrollment & Verification

2-Step Enrollment & Verification

3D face recognition uses depth map information of the user’s face. A facial depth map is a set of thousands of coordinates mapped in threedimensional space describing the contours of the surface of the user’s head relative to a single point of view in front of the user. This depth map may be compared with a reference depth map of the user’s face to authenticate the user.