This project implements a ResNet-34 like architecture using the tensorflow.js core API ([@tensorflow/tfjs-core](https://github.com/tensorflow/tfjs-core)) for realtime face recognition in the browser. 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.



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

``` html

<script src="face-recognition.min.js"></script>

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

const weights = new Float32Array(res.data)

const net = facerecognition.faceRecognitionNet(weights)

Compute and compare two 150 x 150 sized face images:

``` javascript

const imgData1 = ...

const imgData2 = ...

const descriptor1 = await net.computeFaceDescriptor(imgData1)

const descriptor2 = await net.computeFaceDescriptor(imgData2)

const distance = facerecognition.computeFaceDescriptor(descriptor1, descriptor2)

if (distance < 0.6)

console.log('match')

console.log('no match')

You can also get the face descriptor data synchronously:

``` javascript

const desc = net.computeFaceDescriptorSync(imgData1)

Or simply obtain the tensor:

``` javascript

const t = net.forward(imgData1)