An ML model capable of analyzing videos frame by frame to detect human faces
The Video face detector is a powerful ML model that analyzes videos frame by frame to detect the presence of human faces. By accurately identifying faces and providing timestamps for their appearances, the model enhances the video asset management process, enabling users to locate the specific point in the video where human faces appear.
When a human face is detected, the returns the number of faces present in the frame along with the timestamp. This feature allows users to pinpoint specific moments in the video where human faces initially appear.
This detector can be used in the following sample scenarios:
Video analytics and insights - The model provides valuable insights from video content.
Automated video tagging - Videos can be automatically tagged based on the presence of human faces. This simplifies content organization, making it easier for users to search and retrieve videos featuring individuals.
Personalized video recommendations - The face detection results may be used to offer personalized video recommendations to users. By understanding viewer preferences for human presence, relevant content can be suggested, that enhances user experience and engagement.
Advanced video search and filtering - The face detection results allow users to filter and search for videos containing groups of people.
An up-to-date reference with all API endpoints is available here: