**JavaScript API for face detection and face recognition in the browser implemented on top of the tensorflow.js core API ([tensorflow/tfjs-core](https://github.com/tensorflow/tfjs-core))**

* **[Running the Examples](#running-the-examples)**

* **[About Face Detection](#about-face-detection)**

* **[About Face Recognition](#about-face-recognition)**

* **[Usage](#usage)**





<a name="running-the-examples"></a>

``` bash

cd examples

npm i

npm start

Browse to http://localhost:3000/.

<a name="about-face-detection"></a>

For face recognition, this project implements a SSD (Single Shot Multibox Detector) based on MobileNetV1. The neural net will compute the locations of each face in an image and will return the bounding boxes together with it's probability for each face.

The face detection model has been trained on the [WIDERFACE dataset](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) and the weights are provided by [yeephycho](https://github.com/yeephycho) in [this](https://github.com/yeephycho/tensorflow-face-detection) repo.

<a name="about-face-recognition"></a>

For face recognition, a ResNet-34 like architecture is implemented to compute a face descriptor (a feature vector with 128 values) from any given face image, which is used to describe the characteristics of a persons face. The model is **not** limited to the set of faces used for training, meaning you can use it for face recognition of any person, for example yourself. You can determine the similarity of two arbitrary faces by comparing their face descriptors, for example by computing the euclidean distance or using any other classifier of your choice.

The neural net is equivalent to the **FaceRecognizerNet** used in [face-recognition.js](https://github.com/justadudewhohacks/face-recognition.js) and the net used in the [dlib](https://github.com/davisking/dlib/blob/master/examples/dnn_face_recognition_ex.cpp) face recognition example. The weights have been trained by [davisking](https://github.com/davisking) and the model achieves a prediction accuracy of 99.38% on the LFW (Labeled Faces in the Wild) benchmark for face recognition.

<a name="usage"></a>

Get the latest build from dist/face-api.js or dist/face-api.min.js and include the script:

<script src="face-api.js"></script>

Or install the package:

``` bash

npm i face-api

Download the weights file from your server and initialize the net (note, that your server has to host the *face_detection_model.weights* file).

``` javascript

const res = await axios.get('face_detection_model.weights', { responseType: 'arraybuffer' })

const weights = new Float32Array(res.data)

const detectionNet = faceapi.faceDetectionNet(weights)

Detect faces and get the bounding boxes and scores:

``` javascript

const minConfidence = 0.8

const maxResults = 10

const myImg = document.getElementById('myImg')

const detections = await detectionNet.locateFaces(myImg, minConfidence, maxResults)

Draw the detected faces to a canvas:

const detectionsForSize = detections.map(det => det.forSize(myImg.width, myImg.height))

const canvas = document.getElementById('overlay')

canvas.width = myImg.width

canvas.height = myImg.height

faceapi.drawDetection(canvas, detectionsForSize, { withScore: false })

You can also obtain the tensors of the unfiltered bounding boxes and scores for each image in the batch (tensors have to be disposed manually):

``` javascript

const { boxes, scores } = detectionNet.forward('myImg')

Download the weights file from your server and initialize the net (note, that your server has to host the *face_recognition_model.weights* file).

``` javascript

const recognitionNet = faceapi.faceRecognitionNet(weights)

Compute and compare the descriptors of two face images:

const descriptor1 = await recognitionNet.computeFaceDescriptor('myImg')

const descriptor2 = await recognitionNet.computeFaceDescriptor(document.getElementById('myCanvas'))

const desc = recognitionNet.computeFaceDescriptorSync('myImg')

Or simply obtain the tensor (tensor has to be disposed manually):

const t = recognitionNet.forward('myImg')

``` javascript

const detections = await detectionNet.locateFaces(input)

const faceTensors = await faceapi.extractFaceTensors(input, detections)

const descriptors = await Promise.all(faceTensors.map(t => recognitionNet.computeFaceDescriptor(t)))

faceTensors.forEach(t => t.dispose())