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Face Tracking Camera

Everything about Face Tracking Camera


What is a Face Tracking Camera?

A Face Tracking Camera is a camera that uses algorithms to locate faces within images or video footage. Some Face Tracking cameras are only able to identify the presence of a face (face detection), while others are able to identify a face and recognize it by comparing it to the faces stored in a database (face recognition).

How do Face Tracking cameras work?

These cameras contain software that enables them to identify facial features, such as eyes, nose, cheekbones, and jaw. They can also be linked with computers running face tracking software, allowing the captured video to be used for audience measurement, traffic analysis, emotion recognition, face recognition, and more.

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Face Tracking with Webcams

Just like other Face Tracking Cameras, webcams can be linked with face tracking or eye tracking software to detect faces and track eye movements.

Because webcams are readily available and built into most laptops, they are a great resource to use when analyzing how people interact with web pages and apps. By using webcams, it is possible to conduct user experience testing without requiring the users to travel to a lab, for example.

It is important to keep in mind, however, that webcams currently provide a lower degree of accuracy than the specialized eye tracking cameras that rely on infrared technology. Eye tracking studies conducted using webcams are therefore best suited to early-stage research.

Webcams also work well with face tracking software such as Sightcorp’s CrowdSight Toolkit, which detects and analyzes faces in real time.

Where are Face Tracking Cameras used?

Face Tracking Cameras can be used in the following ways:

  • By retailers who want to analyze the visitors and customers in their stores
  • By digital screen network owners who want to analyze their audience and track the performance of content across different locations
  • By individuals and businesses who want to increase the security in and around their premises
  • By smartphone manufacturers looking to offer new interactive user experiences and to enhance device and mobile application security
  • By banks and other institutions who need to be able to identify their customers accurately and detect identity fraud quickly
  • By airports to increase the efficiency and security at the customs


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Technical Specifications

The table below shows how FaceMatch SDK performs on the Labelled Faces in the Wild (LFW) dataset:

FPRTPRThreshold (Inverse of distance)
0.10.99900 ±0.002130.55448
0.010.99667 ±0.005370.59791
0.0010.99367 ±0.006050.62989

FPR = False Positive Rate
TPR = True Positive Rate

These results are an indication only and are based on the specific dataset Labelled Faces in the Wild. Customers can expect similar performance, with possible variations due to hardware and the availability of annotated data.