Сreating a system that would be able to work with a video stream from a camera and:
a) detect human faces in every frame and distinguish them from the background;
b) recognize faces and compare them to descriptive vectors in the database;
c) return a signal with information if a face is in the database or not.
To upload a new dataset into the database the administrator will use a simple Docker script that picks all the images and metadata from a specified folder.
We have researched the statistics on the topic of face recognition and FPR/FNR ratio balance and the theory behind it to find the best system sensitivity settings. Then we have deployed the system on a test stand in our office and tuned that balance even further. Thus we have achieved the best system performance together with a guarantee that the FPR will not exceed 2%.
The first idea was to use some celebrities’ content (videos and images available for free usage) to virtually simulate and test the facial recognition video stream input. But then we decided to test it ourselves, so we have purchased and assembled all the necessary hardware, placed a camera in our office at an angle recommended by the client, hooked it up with the employees profile images from our ERP database and tested it on the company employees. This allowed us to be convinced in the quality of the product being developed.
Our DevOps made a great job performing the smoothest deployment possible and provided all the necessary customer support to avoid any possible issues in operation. We have created a detailed guide on hardware and camera setup requirements, database update guide, and helped the customer step by step to make the system working in its best shape.
The project was tested and accepted by the client. After some minor improvements, the system was launched on several facilities in Mexico, where the staff turnover sometimes exceeds 100% per year.
Our system helps to solve some of the problems related to the staff turnover by simplifying the process of the employee database update and eliminating the need of mesmerizing every worker personally. It also improves security measures and accelerates the checkpoint passage.