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Face landmark detection guide

Face Landmarker task

The MediaPipe Face Landmarker task lets you detect face landmarks and facial expressions in images and videos. You can use this task to identify human facial expressions, apply facial filters and effects, and create virtual avatars. This task uses machine learning (ML) models that can work with single images or a continuous stream of images. The task outputs 3-dimensional face landmarks, blendshape scores (coefficients representing facial expression) to infer detailed facial surfaces in real-time, and transformation matrices to perform the transformations required for effects rendering.

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Get Started

Start using this task by following one of the implementation guides for your target platform. These platform-specific guides walk you through a basic implementation of this task, including a recommended model, and code example with recommended configuration options:

Task details

This section describes the capabilities, inputs, outputs, and configuration options of this task.


  • Input image processing - Processing includes image rotation, resizing, normalization, and color space conversion.
  • Score threshold - Filter results based on prediction scores.
Task inputs Task outputs
The Face Landmarker accepts an input of one of the following data types:
  • Still images
  • Decoded video frames
  • Live video feed
The Face Landmarker outputs the following results:
  • Bounding boxes for detected faces in an image frame.
  • A complete face mesh for each detected face, with blendshape scores denoting facial expressions and coordinates for facial landmarks.

Configurations options

This task has the following configuration options:

Option Name Description Value Range Default Value
running_mode Sets the running mode for the task. The landmarker has the following modes:
  • IMAGE: The mode for recognizing face landmarks on single image inputs.
  • VIDEO: The mode for recognizing face landmarks on the decoded frames of a video.
  • LIVE_STREAM: The mode for recognizing face landmarks on a live stream of input data, such as from camera. In this mode, result_callback must be called to set up a listener to receive the recognition results asynchronously.
num_faces The maximum number of faces that can be detected by the the FaceLandmarker. Smoothing is only applied when num_faces is set to 1. Integer > 0 1
min_face_detection_confidence The minimum confidence score for the face detection to be considered successful. Float [0.0,1.0] 0.5
min_face_presence_confidence The minimum confidence score of face presence score in the face landmark detection. Float [0.0,1.0] 0.5
min_tracking_confidence The minimum confidence score for the face tracking to be considered successful. Float [0.0,1.0] 0.5
output_face_blendshapes Whether Face Landmarker outputs face blendshapes. Face blendshapes are used for rendering the 3D face model. Boolean False
output_facial_transformation_matrixes Whether FaceLandmarker outputs the facial transformation matrix. FaceLandmarker uses the matrix to transform the face landmarks from a canonical face model to the detected face, so users can apply effects on the detected landmarks. Boolean False
result_callback Sets the result listener to receive the landmarker results asynchronously when FaceLandmarker is in the live stream mode. Can only be used when running mode is set to LIVE_STREAM ResultListener N/A


The Face Landmarker uses a series of models to predict face landmarks. The first model detects faces, a second model locates landmarks on the detected faces, and a third model uses those landmarks to identify facial features and expressions.

The following models are packaged together into a downloadable model bundle:

  • Face detection model: detects the presence of faces with a few key facial landmarks.
  • Face mesh model: adds a complete mapping of the face. The model outputs an estimate of 478 3-dimensional face landmarks.
  • Blendshape prediction model: receives output from the face mesh model predicts 52 blendshape scores, which are coefficients representing facial different expressions.

The face detection model is the BlazeFace short-range model, a lightweight and accurate face detector optimized for mobile GPU inference. For more information, see the Face Detector task.

The image below shows a complete mapping of facial landmarks from the model bundle output.

Face Landmarker keypoints

Model bundle Input shape Data type Model Cards Versions
FaceLandmarker FaceDetector: 192 x 192
FaceMesh-V2: 256 x 256
Blendshape: 1 x 146 x 2
float 16 FaceDetector