Sentinel's algorithm recognise faces in an individual videos frame

Through computer vision and machine learning, we can recognise faces in videos, photos, and the real-world - making it easier than ever to transform the way your business interacts with people. Sentinel is capable of detecting an unlimited number of faces in a frame, making it an ideal solution for security in crowded areas. The speed of the detector does not depend on the number of faces in the frame. Sentinel finds faces, even if there are significant age-related changes, a beard or mustache, glasses, or any other means of partial face concealment. This ensures maximum effectiveness of facial recognition for law enforcement organisations. 

Facial recognition offers several advantages over other biometrics:

  1. Proliferation of digital facial image data: The billions of existing digital facial images from countless sources are extremely useful for algorithm training purposes, particularly for algorithms using machine learning techniques.
  2. Enhancement of manual facial recognition: Biometrics can be used in concert with human visual facial recognition processes, such as comparing a live person to their facial image on their driver’s licence or other ID card. Facial recognition technology can be used to automate or enhance this process and provides greater matching accuracy. For just about any process where a person’s face is used by a human to verify their identity, biometrics can be used to improve it.
  3. Ubiquity of cameras on mobile devices: Nearly all smartphones, tablets, and laptops have built-in front-facing cameras that enable high-quality “selfie” shots. This makes it convenient to collect a live facial recognition sample for comparison against a template.
  4. Conducive for use with other modalities for mobile authentication: Facial image capture using the front-facing camera on a phone can be performed passively and simultaneously during capture of other modalities such as voice and keystroke to improve matching performance and liveness detection.

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