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.
These instructions show you how to use the Face Landmarker for web and JavaScript apps. For more information about the capabilities, models, and configuration options of this task, see the Overview.
Code example
The example code for Face Landmarker provides a complete implementation of this task in JavaScript for your reference. This code helps you test this task and get started on building your own face landmarker app. You can view, run, and edit the Face Landmarker example code using just your web browser.
Setup
This section describes key steps for setting up your development environment specifically to use Face Landmarker. For general information on setting up your web and JavaScript development environment, including platform version requirements, see the Setup guide for web.
JavaScript packages
Face Landmarker code is available through the MediaPipe @mediapipe/tasks-vision
NPM package. You can
find and download these libraries by following the instructions in the platform
Setup guide.
You can install the required packages through NPM using the following command:
npm install @mediapipe/tasks-vision
If you want to import the task code via a content delivery network (CDN) service, add the following code in the <head> tag in your HTML file:
<!-- You can replace JSDeliver with another CDN if you prefer to -->
<head>
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision/vision_bundle.js"
crossorigin="anonymous"></script>
</head>
Model
The MediaPipe Face Landmarker task requires a trained model that is compatible with this task. For more information on available trained models for Face Landmarker, see the task overview Models section.
Select and download a model, and then store it within your project directory:
<dev-project-root>/app/shared/models/
Create the task
Use one of the Face Landmarker createFrom...() functions to
prepare the task for running inferences. Use the createFromModelPath()
function with a relative or absolute path to the trained model file.
If your model is already loaded into memory, you can use the
createFromModelBuffer() method.
The code example below demonstrates using the createFromOptions() function to
set up the task. The createFromOptions function allows you to customize the
Face Landmarker with configuration options. For more information, see
Configuration options.
The following code demonstrates how to build and configure the task with custom options:
const vision = await FilesetResolver.forVisionTasks(
// path/to/wasm/root
"https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@latest/wasm"
);
const faceLandmarker = await faceLandmarker.createFromOptions(
vision,
{
baseOptions: {
modelAssetPath: "path/to/model"
},
runningMode: runningMode
});
Configuration options
This task has the following configuration options for Web and JavaScript applications:
| Option Name | Description | Value Range | Default Value |
|---|---|---|---|
running_mode |
Sets the running mode for the task. There are two
modes: IMAGE: The mode for single image inputs. VIDEO: The mode for decoded frames of a video or on a livestream of input data, such as from a camera. |
{IMAGE, VIDEO} |
IMAGE |
numFaces |
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 |
minFaceDetectionConfidence |
The minimum confidence score for the face detection to be considered successful. | Float [0.0,1.0] |
0.5 |
minFacePresenceConfidence |
The minimum confidence score of face presence score in the face landmark detection. | Float [0.0,1.0] |
0.5 |
minTrackingConfidence |
The minimum confidence score for the face tracking to be considered successful. | Float [0.0,1.0] |
0.5 |
outputFaceBlendshapes |
Whether Face Landmarker outputs face blendshapes. Face blendshapes are used for rendering the 3D face model. | Boolean |
False |
outputFacialTransformationMatrixes |
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 |
Prepare data
Face Landmarker can detect faces in images in any format supported by the host browser. The task also handles data input preprocessing, including resizing, rotation and value normalization. To landmark faces in videos, you can use the API to quickly process one frame at a time, using the timestamp of the frame to determine when the faces occur within the video.
Run the task
The Face Landmarker uses the detect() (with running mode IMAGE) and
detectForVideo() (with running mode VIDEO) methods to trigger
inferences. The task processes the data, attempts to landmark faces, and
then reports the results.
Calls to the Face Landmarker detect() and detectForVideo() methods run
synchronously and block the user interface thread. If you detect faces
in video frames from a device's camera, each detection blocks the main
thread. You can prevent this by implementing web workers to run the detect()
and detectForVideo() methods on another thread.
The following code demonstrates how execute the processing with the task model:
Image
const image = document.getElementById("image") as HTMLImageElement;
const faceLandmarkerResult = faceLandmarker.detect(image);
Video
await faceLandmarker.setOptions({ runningMode: "VIDEO" });
let lastVideoTime = -1;
function renderLoop(): void {
const video = document.getElementById("video");
if (video.currentTime !== lastVideoTime) {
const faceLandmarkerResult = faceLandmarker.detectForVideo(video);
processResults(detections);
lastVideoTime = video.currentTime;
}
requestAnimationFrame(() => {
renderLoop();
});
}
For a more complete implementation of running an Face Landmarker task, see the code example.
Handle and display results
The Face Landmarker returns a result object for each detection run. The result object contains a face mesh for each detected face, with coordinates for each face landmark. Optionally, the result object can also contain blendshapes, which denote facial expressions, and a facial transformation matrix to apply face effects on the detected landmarks.
The following shows an example of the output data from this task:
FaceLandmarkerResult:
face_landmarks:
NormalizedLandmark #0:
x: 0.5971359014511108
y: 0.485361784696579
z: -0.038440968841314316
NormalizedLandmark #1:
x: 0.3302789330482483
y: 0.29289937019348145
z: -0.09489090740680695
... (478 landmarks for each face)
face_blendshapes:
browDownLeft: 0.8296722769737244
browDownRight: 0.8096957206726074
browInnerUp: 0.00035583582939580083
browOuterUpLeft: 0.00035752105759456754
... (52 blendshapes for each face)
facial_transformation_matrixes:
[9.99158978e-01, -1.23036895e-02, 3.91213447e-02, -3.70770246e-01]
[1.66496094e-02, 9.93480563e-01, -1.12779640e-01, 2.27719707e+01]
...
The following image shows a visualization of the task output:
The Face Landmarker example code demonstrates how to display the results returned from the task, see the code example