This node searches the supplied image or video for faces. if a face is found, a mesh is generated then tracked along with the image.

Example Image
Example Nodegraph

CPU Tracking

Face tracking using this method is based on machine learning. It uses a preprocessed database generated from hundreds of different images of faces to recognise new faces in the images given to it. This database is required by the Face Tracker node; it is loaded as a “Machine Learning Database” resource and is set via the “Training Database” parameter.

Face tracking with this method is very is CPU-intensive; face recognition takes 1-2 frames on CPU. In order to keep rendering performant, this is performed in parallel to the rest of the graphics processing in the frame. As such, there is an inherent additional latency in the output of the node – typically two frames, but this may be reduced to one frame using the “Low Latency” option at the cost of reduced frame rate. To simplify use, the image output from the Face Tracking node is delayed so it is in sync with the geometry generated by the node: if you are using the face geometry from the detected face with a Face Tracking Geometry node, you can use the image stored on the Face Tracker node to texture it and they will be in sync.

NVIDIA Face Tracking (RTX Only)

This method uses the NVIDIA RTX AI based face tracking solution to generate a face mesh, and track the points between frames. This features requires an NVIDIA RTX enabled graphics card, as well as the NVIDIA AR drivers (available here). Running on the GPU, this method is far faster than the CPU based alternative and can be used with a 3D Face mesh instead of a 2D mesh.


When the Face Tracking node finds a face it generates a mesh which may be used as geometry by the Face Tracking Geometry node, or used to deform a matching 3D object’s vertices using the Face Tracking Deformer node. The image output by the Face Tracking node can either pass through the original input; or output a mask from the detected face geometry in either the RGB or Alpha channels. Only one face can be detected at a time, so multi-face tracking is currently not supported.

The Face Tracker also analyses the detected face pose to determine whether the eyes and mouth are open, the positions of several key points such as the eyes, and the overall approximate rotation and scale of the face. These values are available as envelopes that can be extracted from the node via an Extractor modifier and can, for example, be linked to Image2D nodes in order to attach 2D images to points on the face. The Face Found envelope is set to 0 if no face is found, or 1 if there is.

To improve performance further, there is the option to downsample the input image before face detection – as this typically does not have to be processed at full resolution to find faces. Downsample Steps defines the number of downsample iterations: 1 means half width, half height and so on.

It must be noted that face tracking, while reasonably solid and robust, is not perfect. There is a degree of tolerance but care must be taken with the input feed to ensure that the algorithm is able to perform to its best. In particular:
- Face detection works best in good, balanced, even lighting, with a subject facing straight on to the camera and good image quality. The technique is not intended to work with moving cameras but is tolerant to some degree.
- Excessive facial hair, hats, novelty glasses, low frame rates and fast motion may impact the result.
- The face must be at least 300 pixels high in the source image. Smaller faces will most likely not be detected.
- The face detection algorithm can detect multiple faces in a single image, but at present only one face is tracked. To keep consistency the largest face in the image is chosen each frame.


Name Description
Preview In Viewport Preview the effect blended with alpha in the viewport.
Preview RGB In Viewport Preview the RGB values in the viewport.
Preview Alpha In Viewport Preview the alpha values in the viewport.
Active Turn the effect on or off. Values < 0.1 will turn the effect off, and deferred rendering must be on.
Downsample Steps The number of times the input image is halved in size before face detection is executed – to increase performance.
Frame Skips The number of input frames that are skipped. A 60hz feed may be processed at 30hz to increase overall system performance.
Low Latency Reduce the number of frames between processing and getting the result, at the cost of reduced overall performance.
Output Image Mode Defines the image generated by the node.
  • Original Image, The original image is output with no modifications.
  • Face Mask, A white mask is generated against a black background.
  • Face Mask + RGB, A mask is generated and multiplied against the original image, against a black background.
  • Original Image with Alpha Mask, the original image is output, with the mask applied to the alpha.
Show Bounding Boxes Render bounding boxes of detected faces on screen.
Reset Face When Lost Reset the face data when a face cannot be found.
Solver Which solver to use for Face tracking.
  • CPU, Use the CPU solver. Slower, but Hardware agnostic.
  • NVIDIA Face Tracking (RTX Only), Use an Nvidia face tracking solution. Faster, but only functions with an RTX GPU.
Training Database The Machine Learning Database resource – required for CPU face tracking to work.


Name Description Typical Node Input
Effect Mask Mask out areas the effect wont affect the image. Image Plane
Alpha Image Add an image to control the alpha values of the image. Video Loader


There are no outputs from this node.