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Face Recognition SDK for Surveillance System

Robbery and burglary prevention by 73%.

face recognition SDK for a treasure gallery
Key Details

Robbery and burglary prevention by 73%.

  • Challenge
    Provide accurate face recognition for security system
  • Solution
    Customized face recognition security SDK
  • Technologies and tools
    C++, Python, PyTorch, OpenCV, NVIDIA TensorRT


To date, the black market of art is one of the most profitable yet unregulated markets in the world. FBI states that collectables worth billions of dollars get stolen every year. Art traffickers break into museums to steal pieces of art and place it on the market. Paintings and sculptures make up the most of stolen pieces.

The client is a famous treasure gallery. It displays more than 15, 000 or works of art. Art theft, vandalism, smuggling, looting and raidings are the issues they’re concerned with.
They needed a tech weapon to tackle art crime and protect cultural heritage. That’s why they started looking for a reliable facial recognition service vendor. Specifically, the gallery wanted to use facial recognition for surveillance measures.

Challenge: provide accurate face recognition for security system

At first, the client had an idea of building a face recognition security system project. After a few brainstorming sessions, our team of computer vision experts came up with a solution. We suggested the client customize our face recognition SDK for their security system. Our solution was aimed at providing better surveillance in the gallery.

Solution: customized face recognition security SDK

During this face recognition security system project, our team of engineers has proved their senior-level expertise.

Our SDK coupled with the client’s system was expected to perform the following tasks:

Employee access control

  1. We worked with the employee database that contained their face photos (a few photos for every person).
  2. Next, we tuned in the devices and the camera at the entrance. To do that, we used NVIDIA Jetson Nano.
  3. Then we integrated it with the gallery’s employee access system.

As a result, we implemented facial recognition into the security system of the gallery. Our solution has made it possible to detect and recognize employees right at the gallery entrance. The solution identifies the employee in the crowd based on their facial features – eyes, eyebrows, nose, lips, chin, etc. The results get checked against the employee database. If abnormalities are not detected, the employee lets in. This is how facial recognition can be used in safety measures.

Strengthening the client’s system

  1. We worked with the database of art traffickers – the gallery has handed it out to us.
  2. Cameras and devices. We had to process video streams from multiple cameras. Then we tuned it in with the gallery’s system. It checks visitors against the database of art traffickers. If there’s any similarity detected, the system notifies the management.

In both tasks, our SDK was used for face detection and recognition. All other effects were achieved by tuning it with the client’s infrastructure. This approach has significantly smartened the development pace.

These were the necessary tech requirements for the client’s system for SDK integration:

Installations: doesn’t require special skills or knowledge. To integrate our SDK into the client’s infrastructure, we used the Docker container. it’s safe and reliable and uses system resources wisely.
Devices: Nvidia Jetson Nano и Jetson Xavier
Resolution: HD/FULL HD
Number of cameras: multiple

Result: improved surveillance system for the gallery

Within a tight time frame, we’ve successfully completed this facial recognition security system project. The collaboration with InData Labs has brought the client the following benefits:

  • improved security against art theft, smuggling and raiding
  • robbery and burglary prevention by 73%
  • biometric employee access control
  • reduced workforce costs

Scale Up Your Business with Our Face Recognition Solutions

  • Security
  • Face Recognition
  • Computer Vision

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