Face recognition for video — find every appearance of a person
ClipCatalog detects faces in your videos, groups them by person, and lets you filter your entire library to see every clip where someone appears. Facial recognition is optional, runs entirely on your Windows machine, and your footage never leaves your computer.
Try ClipCatalog free — up to 500 videos
No account required. Your footage stays on your computer.
Find people fast
Need every clip with a guest, collaborator, or family member? Select a person and review all matching clips instantly — no scrubbing through hours of footage.
Works across your whole library
Face groups span your entire indexed library — across projects, drives, and years of footage. Great for long-running series, recurring collaborators, and multi-year archives.
100% local — no cloud uploads
Face detection, embedding, and grouping all run on your computer. Nothing is uploaded. Your face data is stored in an encrypted local database and a local FAISS index.
How face recognition works in ClipCatalog
ClipCatalog uses on-device AI models to detect faces and compute face embeddings — mathematical representations of each face. These embeddings are stored in a local FAISS index and compared to group faces by person, so you can search and filter your library by who appears on screen.
Enable face detection
Toggle face detection on in Settings. A confirmation dialog explains the feature and its implications before enabling it.
Faces are detected & grouped
During processing, ClipCatalog detects faces using YuNet and computes embeddings with SFace — both running locally via OpenCV DNN. Similar faces are clustered into person groups.
Search by person
Select one or more people in the search panel to see all matching clips. If you select multiple people, switch All/Any matching (AND/OR). Combine with tags, transcripts, date, and other filters to narrow down fast.
When and how you may use face recognition
Face recognition in ClipCatalog is designed to be transparent and fully under your control. Before enabling it, the app shows a confirmation dialog with the following statement:
"Analyze faces in my videos locally to group similar faces so I can find videos with the same person.
This may be considered biometric data and can be regulated in some countries.
I am responsible for using this feature in compliance with applicable laws.
I can turn it off at any time and clear existing face data."
Opt-in only — disabled by default
Face detection is off when you first install ClipCatalog. It only activates when you deliberately enable it in Settings and confirm you understand the implications. You can turn it off again at any time — existing face data is preserved until you choose to delete it.
Biometric data considerations
Face embeddings — mathematical representations of facial features — may be classified as biometric data under laws like the EU's GDPR, Illinois' Biometric Information Privacy Act (BIPA), or similar regulations in other jurisdictions. Because ClipCatalog processes everything locally on your machine and never uploads face data, you retain full control. However, you are responsible for ensuring your use complies with the laws that apply to you.
Local processing — no cloud involvement
All face detection, embedding computation, and identification happen on your computer. Face crops, embeddings, and the FAISS index are stored locally in your encrypted ClipCatalog database folder. No face data is ever sent to a server or cloud service.
Full data control — delete anytime
You can delete all face data at any time from Settings: face images, detections, person groups, and the FAISS face index are all removed. If you re-enable face detection later, videos will need to be reprocessed. This gives you a clean way to remove all biometric data if you change your mind or if your circumstances change.
Real-world workflows
Vloggers & recurring guests
You've filmed dozens of episodes with a returning collaborator. Instead of scrolling through every project folder, select their face and see every clip they appear in — across all shoot days and all drives.
Wedding & event coverage
Hundreds of clips from a wedding day. Find every shot of the couple, the best man, or a guest across ceremony, reception, and candid footage — without remembering which card or folder it was on.
Interview series & podcasts
Running a long-form interview series? Filter by a guest's face to pull every appearance across seasons. Combine with transcript search to find the exact moment they said key words you remember.
Family & personal archives
Years of family footage across holidays, birthdays, and trips. See every video where a specific family member appears — great for making compilations or finding that one clip you remember but can't locate.
What to expect from face recognition
Grouping, not perfect ID
Face recognition groups similar-looking faces so you can find clips by person. It uses k-NN voting with thresholds and margins to reduce false matches, and caps embeddings per person to avoid bias from overrepresented faces. Occasional mis-groups can happen with blurry, partial, or heavily obscured faces.
Works from thumbnails
Face detection runs on sampled frames (thumbnails), not every single frame of video. This keeps processing practical for large libraries while still catching the most visible face appearances. Very brief on-screen moments may not be detected.
Optional GPU acceleration
Face detection and embedding use OpenCV DNN, with optional OpenCL acceleration when available. If your system doesn't support OpenCL, the app falls back to CPU automatically — slower, but it still works. Learn about GPU acceleration →
Combine with other filters
Face search is most powerful when layered with other ClipCatalog filters. Find clips where a specific person appears and a keyword was spoken, and a visual tag matches, and it's from a particular date range or folder. Explore all search filters →
If face detection is disabled
When Face Detection is off, the “Footage type” filter won’t be available, and Highlight Score uses fewer signals — so it may be less accurate. You can re-enable Face Detection in Settings at any time. Learn about search filters →
Frequently asked questions
No — face recognition is completely optional. You can use ClipCatalog for detected content, transcript search, and all other features without ever enabling face detection.
No. Face detection, embedding, and grouping all happen locally on your computer. Your footage and face data never leave your machine.
Open Settings and toggle "Face Detection" on. You’ll see a confirmation dialog explaining what the feature does, that face data may be considered biometric data in some jurisdictions, and that you can turn it off and delete face data at any time.
Yes. In Settings you can delete all detected faces, person groups, and the face index with one click. Videos will need to be re-processed if you enable face detection again later.
Yes. You can layer face/person filters with transcript words, detected content, date ranges, folders, resolution, frame rate, and more — so you can find exactly the clip you need.
ClipCatalog uses a smart matching algorithm designed to reduce false matches and avoid bias from overrepresented faces. It’s built to get you to the right set of clips quickly, though occasional mis-groups can happen — especially with low-quality or partial faces.
Face detection and embedding use OpenCV DNN with optional OpenCL acceleration. If OpenCL isn’t available on your system, it falls back to CPU automatically.
In many jurisdictions, face embeddings and facial geometry are classified as biometric data and may be subject to privacy regulations such as GDPR (EU), BIPA (Illinois, US), or similar laws. ClipCatalog processes everything locally and gives you full control, but you are responsible for using this feature in compliance with applicable laws in your region.
Even more powerful together
Face recognition is one search dimension in ClipCatalog. The real advantage is combining it with other filters to go from thousands of clips to exactly what you need.
Find clips by what was said — perfect for interviews, sound bites, and voiceover takes.
Search by what's on screen — scenes, objects, and actions, tagged automatically.
Face data persists even when drives are unplugged — reconnect and search again.
Layer face filters with date, folder, resolution, frame rate, duration, and more.
Best for
- YouTubers & vloggers with recurring guests and collaborators.
- Wedding and event videographers searching hundreds of clips for specific people.
- Filmmakers & editors managing multi-year interview or documentary archives.
- Family archivists organizing years of personal footage by who's in each clip.
- Small teams that film recurring subjects or clients across projects.
Try it with one folder
The best way to see if face recognition works for your footage: enable it in Settings, process a single project folder, and try finding a person across your clips.
Understanding face recognition for video
Whether you call it face search, person detection, or facial recognition for video — the idea is the same: let software identify who's on screen so you can find clips by person instead of by file name or memory.
Why face search matters for video editors
You know you filmed a great reaction shot of a specific guest, but can't remember the file name, folder, or even which drive it's on. Face recognition turns "I know it's somewhere" into a direct filter by person — saving you from scrubbing through hours of footage trying to find it.
Local vs. cloud face recognition
Most face recognition services require uploading footage to a cloud API. For video creators working with client footage, personal content, or simply large files, that's often a non-starter. ClipCatalog runs all face processing on your own hardware — your footage and face data stay on your machine. More about local-first privacy →
How face embeddings work
When ClipCatalog detects a face, it computes an embedding — a compact mathematical representation of that face's features. These embeddings are stored in a local FAISS index, making it fast to compare new faces against all previously seen ones. Similar embeddings are grouped together as the same person, which is how search-by-person works.
Practical limitations
Face recognition works best when faces are clearly visible, well-lit, and facing roughly toward the camera. It can struggle with distant faces, heavy shadows, motion blur, or extreme angles. The system works from sampled frames, so very brief appearances may be missed. Knowing these limitations helps you search smarter and set realistic expectations.
Try ClipCatalog free — up to 500 videos
No account required. Your footage stays on your computer.