How I Built an AI-Powered Tool to Find People Across Thousands of Photos and Videos

Finding a specific person across hundreds or thousands of photos and videos can be surprisingly time-consuming.

Imagine you're a photographer who has just finished shooting a wedding, a teacher who has collected hundreds of school event photos, or a parent trying to find pictures of your child in a large collection of kindergarten photos.

Manually opening every image and video to find someone is not practical.

I wanted to solve this problem, so I built SnapByFace.

SnapByFace is an AI-powered face search tool that helps you find a specific person across large collections of photos and videos.

Instead of manually browsing every file, you can select a face and let the system search for matching faces throughout your media collection.

Find all photos containing a particular client without manually reviewing thousands of images.

Search for your child across photos and videos shared by schools or kindergartens.

Organize large collections of event photos and help parents find pictures of their children.

Find specific attendees across large collections of event media.

I noticed that finding a particular person in a large photo collection is still a surprisingly manual task.

Traditional photo management tools are useful for organizing files, but searching across large collections of photos and videos for a specific person can still be challenging.

I wanted to build something focused specifically on this problem.

If you regularly work with large collections of photos and videos, you can check it out here:

I'd also love to hear how other developers approach face search, local media indexing, and video analysis.