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justadudewhohacksjustadudewhohacks
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‎.gitignore

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node_modules
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_data
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.rpt2_cache

‎.npmignore

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node_modules
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.rpt2_cache
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examples
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test
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proto
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weights

‎README.md

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# face-recognition.min.js
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# face-api.js
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**face recognition API for the browser with tensorflow.js**
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**JavaScript API for face detection and face recognition in the browser with tensorflow.js**
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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.
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``` javascript
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const res = await axios.get('face_recognition_model.weights', { responseType: 'arraybuffer' })
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const weights = new Float32Array(res.data)
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const net = facerecognition.faceRecognitionNet(weights)
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const net = faceapi.faceRecognitionNet(weights)
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```
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Compute and compare two 150 x 150 sized face images:
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const descriptor1 = await net.computeFaceDescriptor(imgData1)
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const descriptor2 = await net.computeFaceDescriptor(imgData2)
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const distance = facerecognition.euclidianDistance(descriptor1, descriptor2)
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const distance = faceapi.euclidianDistance(descriptor1, descriptor2)
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if (distance < 0.6)
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console.log('match')

‎dist/face-recognition.js renamed to ‎dist/face-api.js

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(function (global, factory) {
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typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('crypto')) :
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typeof define === 'function' && define.amd ? define(['exports', 'crypto'], factory) :
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(factory((global.facerecognition = global.facerecognition || {}),global.crypto));
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(factory((global.faceapi = global.faceapi || {}),global.crypto));
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}(this, (function (exports,crypto) { 'use strict';
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crypto = crypto && crypto.hasOwnProperty('default') ? crypto['default'] : crypto;
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seedrandom.xor4096 = xor4096;
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seedrandom.tychei = tychei;
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var C__Users_user_dev_faceRecognition_min_js_node_modules_seedrandom = seedrandom;
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var C__Users_user_dev_faceApi_js_node_modules_seedrandom = seedrandom;
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var seedrandom$1 = /*#__PURE__*/Object.freeze({
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default: C__Users_user_dev_faceRecognition_min_js_node_modules_seedrandom,
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__moduleExports: C__Users_user_dev_faceRecognition_min_js_node_modules_seedrandom
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default: C__Users_user_dev_faceApi_js_node_modules_seedrandom,
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__moduleExports: C__Users_user_dev_faceApi_js_node_modules_seedrandom
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});
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var MPRandGauss = (function () {

‎dist/face-api.min.js

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‎dist/face-recognition.min.js

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‎dist/transformInputs.d.ts

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‎dist/transformInputs.js

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‎dist/transformInputs.js.map

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‎examples/public/commons.js

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async function initFaceDetectionNet() {
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const res = await axios.get('face_detection_model.weights', { responseType: 'arraybuffer' })
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const weights = new Float32Array(res.data)
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return facerecognition.faceDetectionNet(weights)
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return faceapi.faceDetectionNet(weights)
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}
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async function initFaceRecognitionNet() {
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const res = await axios.get('face_recognition_model.weights', { responseType: 'arraybuffer' })
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const weights = new Float32Array(res.data)
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return facerecognition.faceRecognitionNet(weights)
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return faceapi.faceRecognitionNet(weights)
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}
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// fetch first image of each class and compute their descriptors
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async className => {
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const descriptors = []
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for (let i = 1; i < (numImagesForTraining + 1); i++) {
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const img = await facerecognition.bufferToImage(
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const img = await faceapi.bufferToImage(
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await fetchImage(getFaceImageUri(className, i))
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)
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descriptors.push(await net.computeFaceDescriptor(img))
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function getBestMatch(descriptorsByClass, queryDescriptor) {
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function computeMeanDistance(descriptorsOfClass) {
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return facerecognition.round(
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return faceapi.round(
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descriptorsOfClass
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.map(d => facerecognition.euclideanDistance(d, queryDescriptor))
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.map(d => faceapi.euclideanDistance(d, queryDescriptor))
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.reduce((d1, d2) => d1 + d2, 0)
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/ (descriptorsOfClass.length || 1)
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)

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