实践 | Face-API实现人脸识别（附源代码）
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作者：Edison_G
参考face-api.js
https://github.com/justadudewhohacks/face-api.js#face-api.js-for-the-browse
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从此不迷路
计算机视觉研究院
功能
第一阶段实现对图片中人脸的识别并打上标签(比如：人名)
第二阶段使用摄像头实现对人物的识别，比如典型的应用做一个人脸考勤的系统
资源
face-api.js https://github.com/justadudewhohacks/face-api.js/
Face-api.js 是一个 JavaScript API，是基于 tensorflow.js 核心 API 的人脸检测和人脸识别的浏览器实现。它实现了一系列的卷积神经网络（CNN），针对网络和移动设备进行了优化。
filepond https://github.com/pqina/filepond
是一个 JavaScript 文件上传库。可以拖入上传文件，并且会对图像进行优化以加快上传速度。让用户体验到出色、进度可见、如丝般顺畅的用户体验。确实很酷的一款上传图片的开源产品。
fancyBox https://fancyapps.com/fancybox/3/
是一个 JavaScript 库，它以优雅的方式展示图片，视频和一些 html 内容。它包含你所期望的一切特性 —— 支持触屏，响应式和高度自定义。
设计思路
准备一个人脸数据库，上传照片，并打上标签（人名），最好但是单张脸的照片，测试的时候可以同时对一张照片上的多个人物进行识别；
提取人脸数据库中的照片和标签进行量化处理，转化成一堆数字，这样就可以进行比较匹配；
使用一张照片来测试一下匹配程度。
代码具体实践步骤
还是先来看看代码吧，做这类开发，并没有想象中的那么难，因为难的核心别人都已经帮你实现了，所以和普通的程序开发没有什么不同，熟练掌握这些api的方法和功能就可以做出非常实用并且非常酷炫的产品。
1、准备素材：下载每个人物的图片进行分类
2、上传服务器数据库
3、测试
Face Landmark Detection
Face Recognition
Face Expression Recognition
Running the Examples
Clone the repository:
git clone https://github.com/justadudewhohacks/face-api.js.gitRunning the Browser Examples
cd face-api.js/examples/examples-browser
npm i
npm startBrowse to http://localhost:3000/.
Running the Nodejs Examples
cd face-api.js/examples/examples-nodejs
npm iNow run one of the examples using ts-node:
ts-node faceDetection.tsOr simply compile and run them with node:
tsc faceDetection.ts
node faceDetection.js代码·解析
这里对face-api.js类库代码做一下简单的说明。
function dodetectpic() {
$.messager.progress();
//加载训练好的模型（weight，bias）
Promise.all([
faceapi.nets.faceRecognitionNet.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights'),
faceapi.nets.faceLandmark68Net.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights'),
faceapi.nets.faceLandmark68TinyNet.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights'),
faceapi.nets.ssdMobilenetv1.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights'),
faceapi.nets.tinyFaceDetector.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights'),
faceapi.nets.mtcnn.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights'),
//faceapi.nets.tinyYolov.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights')
]).then(async () => {
//在原来图片容器中添加一层用于显示识别的蓝色框框
const container = document.createElement('div')
container.style.position = 'relative'
$('#picmodal').prepend(container)
//先加载维护好的人脸数据(人脸的特征数据和标签，用于后面的比对)
const labeledFaceDescriptors = await loadLabeledImages()
//比对人脸特征数据
const faceMatcher = new faceapi.FaceMatcher(labeledFaceDescriptors, 0.6)
//获取输入图片
let image = document.getElementById('testpic')
//根据图片大小创建一个图层，用于显示方框
let canvas = faceapi.createCanvasFromMedia(image)
//console.log(canvas);
container.prepend(canvas)
const displaySize = { width: image.width, height: image.height }
faceapi.matchDimensions(canvas, displaySize)
//设置需要使用什么算法和参数进行扫描识别图片的人脸特征
const options = new faceapi.SsdMobilenetv1Options({ minConfidence: 0.38 })
//const options = new faceapi.TinyFaceDetectorOptions()
//const options = new faceapi.MtcnnOptions()
//开始获取图片中每一张人脸的特征数据
const detections = await faceapi.detectAllFaces(image, options).withFaceLandmarks().withFaceDescriptors()
//根据人脸轮廓的大小，调整方框的大小
const resizedDetections = faceapi.resizeResults(detections, displaySize)
//开始和事先准备的标签库比对，找出最符合的那个标签
const results = resizedDetections.map(d => faceMatcher.findBestMatch(d.descriptor))
console.log(results)
results.forEach((result, i) => {
//显示比对的结果
const box = resizedDetections[i].detection.box
const drawBox = new faceapi.draw.DrawBox(box, { label: result.toString() })
drawBox.draw(canvas)
console.log(box, drawBox)
})
$.messager.progress('close');
})
}
//读取人脸标签数据
async function loadLabeledImages() {
//获取人脸图片数据,包含：图片+标签
const data = await $.get('/FaceLibs/GetImgData');
//对图片按标签进行分类
const labels = [...new Set(data.map(item => item.Label))]
console.log(labels);
return Promise.all(
labels.map(async label => {
const descriptions = []
const imgs = data.filter(item => item.Label == label);
for (let i = 0; i < imgs.length; i++) {
const item = imgs[i];
const img = await faceapi.fetchImage(`${item.ImgUrl}`)
//console.log(item.ImgUrl, img);
//const detections = await faceapi.detectSingleFace(img).withFaceLandmarks().withFaceDescriptor()
//识别人脸的初始化参数
const options = new faceapi.SsdMobilenetv1Options({ minConfidence:0.38})
//const options = new faceapi.TinyFaceDetectorOptions()
//const options = new faceapi.MtcnnOptions()
//扫描图片中人脸的轮廓数据
const detections = await faceapi.detectSingleFace(img, options).withFaceLandmarks().withFaceDescriptor()
console.log(detections);
if (detections) {
descriptions.push(detections.descriptor)
} else {
console.warn('Unrecognizable face')
}
}
console.log(label, descriptions);
return new faceapi.LabeledFaceDescriptors(label, descriptions)
})
)
}
face-api 类库介绍
face-api 有几个非常重要的方法下面说明一下都是来自 https://github.com/justadudewhohacks/face-api.js/ 的介绍在使用这些方法前必须先加载训练好的模型,这里并不需要自己照片进行训练了，face-api.js应该是在tensorflow.js上改的所以这些训练好的模型应该和python版的tensorflow都是通用的。所有可用的模型都在https://github.com/justadudewhohacks/face-api.js/tree/master/weights 可以找到。
//加载训练好的模型（weight，bias）
// ageGenderNet 识别性别和年龄
// faceExpressionNet 识别表情,开心，沮丧，普通
// faceLandmark68Net 识别脸部特征用于mobilenet算法
// faceLandmark68TinyNet 识别脸部特征用于tiny算法
// faceRecognitionNet 识别人脸
// ssdMobilenetv1 google开源AI算法除库包含分类和线性回归
// tinyFaceDetector 比Google的mobilenet更轻量级，速度更快一点
// mtcnn 多任务CNN算法，一开浏览器就卡死
// tinyYolov2 识别身体轮廓的算法，不知道怎么用
Promise.all([
faceapi.nets.faceRecognitionNet.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights'),
faceapi.nets.faceLandmark68Net.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights'),
faceapi.nets.faceLandmark68TinyNet.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights'),
faceapi.nets.ssdMobilenetv1.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights'),
faceapi.nets.tinyFaceDetector.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights'),
faceapi.nets.mtcnn.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights'),
//faceapi.nets.tinyYolov.loadFromUri('https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights')
]).then(async () => {})
非常重要参数设置，在优化识别性能和比对的正确性上很有帮助，就是需要慢慢的微调。
SsdMobilenetv1Options
export interface ISsdMobilenetv1Options {
// minimum confidence threshold
// default: 0.5
minConfidence?: number
// maximum number of faces to return
// default: 100
maxResults?: number
}
// example
const options = new faceapi.SsdMobilenetv1Options({ minConfidence: 0.8 })
TinyFaceDetectorOptions
export interface ITinyFaceDetectorOptions {
// size at which image is processed, the smaller the faster,
// but less precise in detecting smaller faces, must be divisible
// by 32, common sizes are 128, 160, 224, 320, 416, 512, 608,
// for face tracking via webcam I would recommend using smaller sizes,
// e.g. 128, 160, for detecting smaller faces use larger sizes, e.g. 512, 608
// default: 416
inputSize?: number
// minimum confidence threshold
// default: 0.5
scoreThreshold?: number
}
// example
const options = new faceapi.TinyFaceDetectorOptions({ inputSize: 320 })
MtcnnOptions
export interface IMtcnnOptions {
// minimum face size to expect, the higher the faster processing will be,
// but smaller faces won't be detected
// default: 20
minFaceSize?: number
// the score threshold values used to filter the bounding
// boxes of stage 1, 2 and 3
// default: [0.6, 0.7, 0.7]
scoreThresholds?: number[]
// scale factor used to calculate the scale steps of the image
// pyramid used in stage 1
// default: 0.709
scaleFactor?: number
// number of scaled versions of the input image passed through the CNN
// of the first stage, lower numbers will result in lower inference time,
// but will also be less accurate
// default: 10
maxNumScales?: number
// instead of specifying scaleFactor and maxNumScales you can also
// set the scaleSteps manually
scaleSteps?: number[]
}
// example
const options = new faceapi.MtcnnOptions({ minFaceSize: 100, scaleFactor: 0.8 })
最常用的图片识别方法，想要识别什么就调用相应的方法就好了。
// all faces
await faceapi.detectAllFaces(input)
await faceapi.detectAllFaces(input).withFaceExpressions()
await faceapi.detectAllFaces(input).withFaceLandmarks()
await faceapi.detectAllFaces(input).withFaceLandmarks().withFaceExpressions()
await faceapi.detectAllFaces(input).withFaceLandmarks().withFaceExpressions().withFaceDescriptors()
await faceapi.detectAllFaces(input).withFaceLandmarks().withAgeAndGender().withFaceDescriptors()
await faceapi.detectAllFaces(input).withFaceLandmarks().withFaceExpressions().withAgeAndGender().withFaceDescriptors()
// single face
await faceapi.detectSingleFace(input)
await faceapi.detectSingleFace(input).withFaceExpressions()
await faceapi.detectSingleFace(input).withFaceLandmarks()
await faceapi.detectSingleFace(input).withFaceLandmarks().withFaceExpressions()
await faceapi.detectSingleFace(input).withFaceLandmarks().withFaceExpressions().withFaceDescriptor()
await faceapi.detectSingleFace(input).withFaceLandmarks().withAgeAndGender().withFaceDescriptor()
await faceapi.detectSingleFace(input).withFaceLandmarks().withFaceExpressions().withAgeAndGender().withFaceDescriptor()
Detecting Faces
Detect all faces in an image. Returns Array
const detections = await faceapi.detectAllFaces(input)Detect the face with the highest confidence score in an image. Returns FaceDetection | undefined:
const detection = await faceapi.detectSingleFace(input)By default detectAllFaces and detectSingleFace utilize the SSD Mobilenet V1 Face Detector. You can specify the face detector by passing the corresponding options object:
const detections1 = await faceapi.detectAllFaces(input, new faceapi.SsdMobilenetv1Options())
const detections2 = await faceapi.detectAllFaces(input, new faceapi.TinyFaceDetectorOptions())具体可以参考官方Github具体步骤！
／End.
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