How to Find Anyone in Hours of Event Footage Using AI

You're editing a corporate summit. Three days, six speakers, 400 attendees, 12 camera angles. The client calls - they need "all the footage of the CEO" by end of day.

You have 80 hours of raw files.

This is the scenario that exposes every gap in a video organisation workflow. And it happens constantly - to event videographers, sports shooters, documentary teams, and anyone who captures large-scale events with lots of people.

The old way: scrub and hope

The standard approach: export a rough proxy, skim at 8× speed, manually mark in-points for every frame the CEO appears. For a 3-day summit with 40 hours of footage across multiple cameras, this takes hours. For a large music festival, it can take days.

There's a reason most event editors budget a full day just for the selects pass.

What "find by face" actually means

AI face recognition video tools work by scanning your footage, detecting the faces in each shot, and computing a mathematical representation (an embedding) of that face. When the same face appears in another clip - different lighting, different angle, different camera - the system matches the embeddings and groups the clips together.

The result: instead of scrubbing, you browse. Pick a person, see every clip they appear in.

What Tabor8 does differently: most face search tools show you where a person appears. Tabor8 goes one step further - it cuts the clips and delivers them in a folder. You're not just finding the footage; you're receiving a ready-to-edit package for that person.

Accuracy in the real world

Face recognition doesn't work perfectly in every condition. Here's what to expect:

Works well: Clear forward-facing shots, daylight or well-lit indoor events, footage shot at 24fps or higher.

Works less well: Extreme profile shots, heavy motion blur, very low light, faces partially obscured (masks, hats, sunglasses).

In Tabor8's Grammy test - dense, crowded event footage - the system grouped 42 distinct people across an 8-minute clip. For a typical corporate event or wedding, accuracy is consistently high.

The key: any AI tool will produce some false positives and false negatives. Tabor8 gives you manual tools to merge duplicate person entries and to name the folders yourself, since Tabor8 only numbers them. Human oversight stays in the workflow.

What to look for when choosing a face recognition video tool

Output format: Does it deliver clip files, or just timestamps? Clip files = ready for your NLE. Timestamps = you still have to cut.

Cloud vs. local: Local tools keep data on your machine - good for privacy, but tied to one machine. Cloud tools work from any device, no install needed.

Biometric data handling: Face embeddings are biometric data. Ask: are they deleted after the scan? Stored permanently? Sold to third parties? Tabor8 keeps the embeddings with your project so you can merge people across projects you own or have been given access to, and deletes them when you delete the person or the project. They are never sold, and are handled only by the service providers that run Tabor8.

Supports common camera files: Tabor8 takes MP4, MOV, M4V, MKV and AVI, which covers iPhone, GoPro, DJI and most mirrorless cameras, without conversion.

The real time saving

One event videographer using Tabor8 reported this: "I used to spend a full day on selects for a corporate conference. Now I spend 20 minutes reviewing what Tabor8 produced and the rest of the day actually editing."

That's not a marginal improvement. That's a different job.