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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实践 | Face-API实现人脸识别(附源代码)_第1张图片

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

实践 | Face-API实现人脸识别(附源代码)_第2张图片

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

  1. 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;

  2. 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;

  3. 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

实践 | Face-API实现人脸识别(附源代码)_第3张图片

Running the Examples

Clone the repository:

git clone https://github.com/justadudewhohacks/face-api.js.git

Running the Browser Examples

cd face-api.js/examples/examples-browser
npm i
npm start

Browse to http://localhost:3000/.

Running the Nodejs Examples

cd face-api.js/examples/examples-nodejs
npm i

Now run one of the examples using ts-node:

ts-node faceDetection.ts

Or 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!

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