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@tensorflow-models/face-detection
Advanced tools
This package provides models for running real-time face detection.
Currently, we provide 1 model option:
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.
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
You may reference the demos for code examples. Details for how to run the demos
are included in the demos/
folder.
FAQs
Pretrained face detection model
The npm package @tensorflow-models/face-detection receives a total of 32,230 weekly downloads. As such, @tensorflow-models/face-detection popularity was classified as popular.
We found that @tensorflow-models/face-detection demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 8 open source maintainers collaborating on the project.
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