Face recognition news

Multiuser Intelligent Digital Signage Concept

Multiuser Intelligent Digital Signage Concept: Holographic screen tour and facial recognition


This proof of concept of Intel Digital Signage is designed to let retail customers touch its holographic screen to virtually tour a store, shop for products, learn about sales, read customer reviews, submit their own reviews, and share feedback with family and friends through integration with social networks and cell phones.

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Helium3D is the next 3D step for Natal

Helium3D: 3DTV which recognises somebody sitting in a room, what they wish to view and show different images to different viewers! WOW


A revolutionary interactive 3DTV system is being created by De Montfort University Leicester (DMU), England, researchers. The €4.2 million (approx £3.7 million) project aims to develop a television that can recognise where somebody is sitting in a room and what they wish to view and interact with on their television.

Researchers believe it is a step towards truly interactive 3D video games where gamers use their bodies to control the action without the need for a controller. It could be the next step for Microsoft’s Project Natal.

The project, called HELIUM3D (high efficiency, laser-based, multi-user, multi-modal 3D display) is also exploring ways of allowing viewers who are watching the same television to each view a different channel at the same time and could even let them choose different viewing positions within the image.

For example, groups of people watching a football match in the same room could each pick the part of the stadium from which they would like to experience the action.

Read more about: Helium3D is the next 3D step for Natal

Face Mining Startrek

Face ming star trek: chatbots will recognize us in moving video as we walk!


face ming star trekThe company PittPatt has face mined the TV series Star Trek. Specifically, they applied algorithms in face detection, face tracking and face recognition to 67 Star Trek episodes over three seasons. This process automatically extracts all visible face tracks, and clusters these into a small number of same-person groupings. Currently, PittPatt recognize frontal or near-frontal tracks. In the near future, we will extend our results to non-frontal tracks as well.

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