@tensorflow-models/face-detection

Face Detection

This package provides models for running real-time face detection.

Currently, we provide 1 model option:

MediaPipe FaceDetection:

Demo

MediaPipe FaceDetection can detect multiple faces, each face contains 6 keypoints.

More background information about the package, as well as its performance characteristics on different datasets, can be found here: Short Range Model Card, Sparse Full Range Model Card.

Table of Contents

• How to Run It
• Example Code and Demos

How to Run It

In general there are two steps:

You first create a detector by choosing one of the models from SupportedModels, including MediaPipeFaceDetector.

For example:

const model = faceDetection.SupportedModels.MediaPipeFaceDetector;
const detectorConfig = {
  runtime: 'mediapipe', // or 'tfjs'
}
const detector = await faceDetection.createDetector(model, detectorConfig);

Then you can use the detector to detect faces.

const faces = await detector.estimateFaces(image);

The returned face list contains detected faces for each face in the image. If the model cannot detect any faces, the list will be empty.

For each face, it contains a bounding box of the detected face, as well as an array of keypoints. MediaPipeFaceDetector returns 6 keypoints. Each keypoint contains x and y, as well as a name.

Example output:

[
  {
    box: {
      xMin: 304.6476503248806,
      xMax: 502.5079975897382,
      yMin: 102.16298762367356,
      yMax: 349.035215984403,
      width: 197.86034726485758,
      height: 246.87222836072945
    },
    keypoints: [
      {x: 446.544237446397, y: 256.8054528661723, name: "rightEye"},
      {x: 406.53152857172876, y: 255.8, "leftEye },
      ...
    ],
  }
]

The box represents the bounding box of the face in the image pixel space, with xMin, xMax denoting the x-bounds, yMin, yMax denoting the y-bounds, and width, height are the dimensions of the bounding box.

For the keypoints, x and y represent the actual keypoint position in the image pixel space.

The name provides a label for the keypoint, which are 'rightEye', 'leftEye', 'noseTip', 'mouthCenter', 'rightEarTragion', and 'leftEarTragion' respectively.

Refer to each model's documentation for specific configurations for the model and their performance.

MediaPipeFaceDetector MediaPipe Documentation

MediaPipeFaceDetector TFJS Documentation

Example Code and Demos

You may reference the demos for code examples. Details for how to run the demos are included in the demos/ folder.