The Problem

With COVID-19, other pandemics and terrorism growing threat, governments and law enforcement bodies worldwide are looking for next generation facial recognition systems able to address increasing security concerns in crowded places.

There is a need to move facial recognition systems to large-scale and more unconstrained environments (e.g. large indoor/outdoor places with changing lighting conditions, views...), and to accurately identify people even when wearing medical face masks or presenting other strong facial occlusions.

It is also of the utmost importance to introduce crowd behaviour analytics capacities and detect abnormal events, such as mass panic, stampedes or bottlenecks. Particularly, the automatic monitoring of crowd density, social distance between individuals, occupancy and the number of persons wearing masks becomes vitally important to control the spread of COVID-19 and other pandemics in the future.

The Solution

The FUTURE project aims at bringing to the market a product that will change the rules of facial recognition in crowded scenarios, both in the deployment and in the added value of its analytics.

It solves major pains:

It uses edge computing technology, being early adopter of low-power Deep Neural Network (DNN) accelerators to achieve outstanding processing speed and cost-efficiency.
Thanks to its plug-and-play approach, the solution is easy to deploy in very large-scale environments, reducing time and cost of installation.
It is the first solution in the market able to analyse crowd behaviour and people with masks.
It ensures data privacy and compliance with the EU General Data Protection Regulation (GDPR) and with the proposed Artificial Intelligence Act through privacy by design.
In addition, it is the first solution in the market to monitor COVID-19 (and other pandemics) preventive measures in crowded scenarios: social distancing, occupancy and masks wearing

The Problem

The Solution

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