MediaPipeFaceLandmarker - ComfyUI Built-in Node Documentation

Complete documentation for the MediaPipeFaceLandmarker node in ComfyUI. Learn its inputs, outputs, parameters and usage.

Overview

Detects faces in an image and identifies 468 facial landmarks (key points) on each face using MediaPipe’s BlazeFace and FaceMesh models. It also calculates ARKit-52 blendshape coefficients for facial expression analysis. The node can process multiple images in a batch and outputs both the landmark data and bounding boxes for each detected face.

Inputs

Parameter	Description	Data Type	Required	Range
face_detection_model	The MediaPipe face detection model to use for landmark detection.	FACE_DETECTION_MODEL	Yes
image	The input image or batch of images to detect faces in.	IMAGE	Yes
detector_variant	Face detector range. "short" is tuned for close-up faces (within ~2 m of the camera); "full" covers farther/smaller faces (up to ~5 m) but is slower. "both" runs both detectors and keeps whichever found more faces per frame (~2x detection cost). Default: "short".	COMBO	Yes	"short" "full" "both"
num_faces	Maximum number of faces to return per frame. 0 means no cap (return all detected). Default: 1.	INT	Yes	0 to 16
min_confidence	BlazeFace score threshold. Lower values help catch small or occluded faces. Default: 0.5.	FLOAT	No	0.00 to 1.00
missing_frame_fallback	Per-frame behavior when detection fails in a batch. "empty" leaves the frame faceless. "previous" copies the most recent successful detection. "interpolate" lerps landmarks/bbox/blendshapes between bracketing successful frames. Multi-face: pairs faces across frames by greedy bbox-centre NN. Default: "empty".	COMBO	No	"empty" "previous" "interpolate"

Outputs

Output Name	Description	Data Type
face_landmarks	A structured output containing per-frame face detection results, including 468 facial landmarks, ARKit-52 blendshape coefficients, transformation matrices, and connection sets for mesh visualization.	FACE_LANDMARKS
bboxes	A list of bounding boxes for each detected face, with coordinates (x, y, width, height), label “face”, and confidence score. One list per input frame.	BOUNDING_BOX

This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! Edit on GitHub

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