Computer vision and graphics researcher and professor at Zagreb University
Computer vision researcher and Adjunct Senior Lecturer at Linköping University
Computer graphics researcher and Assistant Professor at Chalmers University
Information coding, video compression and optical networks researcher and Professor at Linköping University
Here at Face Technology Division, we are passionate about reading human faces with technology. We build powerful computer vision algorithms that obtain as many information as possible from human faces in images and videos. Our software tracks human faces, estimates their age, gender and emotions and, finally, recognizes their identity.
Automotive Division was formed exclusively for an R&D collaboration project with a major automotive safety supplier. The division develops smart algorithms for ADAS (Advanced Driver-Assistance System). By using computer vision to detect and classify objects visible by the vehicle’s camera, we are helping make autonomous driving safer.
Founded by computer vision experts, Visage Technologies provided a unique mix of research, knowledge and experience from its very beginning. 17 years and hundreds of projects later, our technology remains at the forefront of face tracking and analysis.
We want to ensure only top quality for our clients. Our partners help us there by implementing our technology into top class, out-of-the-box products.
Our technology is used by more than 200 clients – from start-ups and research institutions to large multinationals. From marketing to robotics, visageSDK is at the very heart of innovative solutions worldwide.
“There are really a lot of use cases for biometrics and verification. The most popular ones are face tracking, iris detection and tracking, fingerprint, and voice recognition.”
“A unique mix of research, knowledge, experience and cooperation, combined with consulting and custom development services have helped the company’s clients make the most of this cutting-edge technology.”
“For a machine to understand facial movement, it needs to understand the many ways a face can look. The algorithms have to be robust to various lighting changes, occlusions, different extreme head rotations,
