Swiss startup Eyeware raises $1.9M funding, aims to integrate 3D eye tracking into consumer applications

Eyeware is a Swiss deep tech startup involved in 3D eye tracking. It is an expert in computer vision and develops software for depth sensing enabled consumer devices. The software lets consumer devices, robots and cars fitted with 3D cameras sense as well as examine the attention of users.

On Thursday, this startup announced that it has raised CHF 1.9 million ($1.9 million) in a seed funding round led by High-Tech Gründerfonds (HTGF), TRUMPF Venture GmbH, Swiss Startup Group AG and Zurich Kantonalbank. Notably, Eyeware is a spin-off of Idiap Research Institute and EPFL.

Founded in 2016 by Kenneth Funes, Serban Mogos and Bastjan Prenaj, Eyeware Tech teamed up with several industrial partners in order to develop its eye tracking applications and prototypes. The recently raised capital will be used by the team to integrate the 3D eye tracking technology into consumer applications. Also, the company is aiming to expand its corporate partnerships to other companies in China and the U.S.

Futuristic factory workplace concept

BionicWorkplace is one of the projects that was presented publicly. It is a futuristic factory workplace concept with a collaborative robotic arm developed by Festo, a German corporate and presented at the Hannover Messe 2018.

Eyeware was present at CES 2019

Eyeware was also present at the recently concluded CES 2019 and was presented as a part of the automotive solutions. The software can be used to autograde time-of-flight (TOF) cameras to analyse the attention of drivers. This system can reduce the number of road accidents due to drivers who lose attention.

GazeSense provides real-time coding

GazeSese is the 3D eye tracking software of Eyeware. It provides real-time gaze coding without the necessity of wearing glasses. It tracks the eye-gaze in 3D using the off-shelf depth-sensing cameras. This way, you can track multiple subjects, label the objects of interest and also track attention towards these objects in real-time.

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