Face detection: Datasets & Models
A computer vision workflow that locates and draws bounding boxes around human faces in images and video streams. Built for security and surveillance applications where reliable face detection is a prerequisite for downstream tasks.
Face detection
This face detection workflow locates human faces in visual media and returns bounding box coordinates for each face it finds. It accepts still images, recorded video, or live camera streams as input, making it suitable for security and surveillance pipelines that require reliable face detection before running recognition, counting, or alerting steps.
Try it out
Powered by general detection model
Run on custom image
Drop an image here or click to upload
Why automate face detection
Manually monitoring surveillance footage for people is slow, error-prone, and does not scale across dozens of cameras running around the clock. Automating face detection lets security teams flag when and where faces appear, trigger downstream recognition, and reduce the hours spent scrubbing video. In access control, crowd monitoring, and perimeter security contexts, a reliable detector turns passive camera infrastructure into an active, queryable system. The result is faster response and consistent coverage that a human operator cannot maintain indefinitely.
Key benefits
Automated face detection delivers consistent, frame-by-frame coverage across large camera networks and integrates with existing IP cameras and video management systems through standard streaming protocols. Using RF-DETR, teams get accurate detection that scales from a single entrance camera to a full facility deployment. Because it plugs into existing surveillance hardware, there is no need to rip and replace infrastructure to add automated monitoring.
How to use it
- Upload an image: Drop any photo into the widget above to try face detection for free, right on this page.
- Preview instant results: The model detects face, head, person, eyes within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
- Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
- Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.
Recommended model for face detection
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for face detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Face Detection project to Roboflow Agent.