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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>第 16 章 人脸识别</title><link rel="stylesheet" type="text/css" href="docbook.css" /><meta name="generator" content="DocBook XSL Stylesheets Vsnapshot" /><meta name="keywords" content="php,pear,pecl,phar, python, , " /><link rel="home" href="index.html" title="Netkiller Python 手札" /><link rel="up" href="index.html" title="部分 III. 人工智能 AI" /><link rel="prev" href="speech/asr.html" title="15.6. Automatic Speech Recognition" /><link rel="next" href="ch16s02.html" title="16.2. 命令行工具" /></head><body><a xmlns="" href="//www.netkiller.cn/">Home</a> | <a xmlns="" href="//netkiller.github.io/">简体中文</a> | <a xmlns="" href="http://netkiller.sourceforge.net/">繁体中文</a> | <a xmlns="" href="/journal/index.html">杂文</a>
| <a xmlns="" href="https://github.com/netkiller">Github</a> | <a xmlns="" href="https://zhuanlan.zhihu.com/netkiller">知乎专栏</a> | <a xmlns="" href="https://www.facebook.com/bg7nyt">Facebook</a> | <a xmlns="" href="http://cn.linkedin.com/in/netkiller/">Linkedin</a> | <a xmlns="" href="https://www.youtube.com/user/bg7nyt/videos">Youtube</a> | <a xmlns="" href="//www.netkiller.cn/home/donations.html">打赏(Donations)</a> | <a xmlns="" href="//www.netkiller.cn/home/about.html">About</a><div class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="3" align="center">第 16 章 人脸识别</th></tr><tr><td width="20%" align="left"><a accesskey="p" href="speech/asr.html">上一页</a> </td><th width="60%" align="center">部分 III. 人工智能 AI</th><td width="20%" align="right"> <a accesskey="n" href="ch16s02.html">下一页</a></td></tr></table><hr /></div><table xmlns=""><tr><td><iframe src="//ghbtns.com/github-btn.html?user=netkiller&repo=netkiller.github.io&type=watch&count=true&size=large" height="30" width="170" frameborder="0" scrolling="0" style="width:170px; height: 30px;" allowTransparency="true"></iframe></td><td><iframe src="//ghbtns.com/github-btn.html?user=netkiller&repo=netkiller.github.io&type=fork&count=true&size=large" height="30" width="170" frameborder="0" scrolling="0" style="width:170px; height: 30px;" allowTransparency="true"></iframe></td><td><iframe src="//ghbtns.com/github-btn.html?user=netkiller&type=follow&count=true&size=large" height="30" width="240" frameborder="0" scrolling="0" style="width:240px; height: 30px;" allowTransparency="true"></iframe></td><td></td><td><a href="https://zhuanlan.zhihu.com/netkiller"><img src="/images/logo/zhihu-card-default.svg" height="25" /></a></td><td valign="middle"><a href="https://zhuanlan.zhihu.com/netkiller">知乎专栏</a></td><td></td><td></td><td></td><td></td></tr></table><div class="chapter"><div class="titlepage"><div><div><h2 class="title"><a id="face_recognition"></a>第 16 章 人脸识别</h2></div></div></div><div class="toc"><p><strong>目录</strong></p><dl class="toc"><dt><span class="section"><a href="face_recognition.html#id1202">16.1. 安装</a></span></dt><dt><span class="section"><a href="ch16s02.html">16.2. 命令行工具</a></span></dt><dd><dl><dt><span class="section"><a href="ch16s02.html#id1203">16.2.1. face_detection - 在单张图片或一个图片文件夹中定位人脸位置</a></span></dt><dt><span class="section"><a href="ch16s02.html#id1204">16.2.2. face_detection - 在单张图片或一个图片文件夹中定位人脸位置</a></span></dt></dl></dd><dt><span class="section"><a href="ch16s03.html">16.3. 定位头像位置</a></span></dt><dt><span class="section"><a href="ch16s04.html">16.4. 人脸比较</a></span></dt><dt><span class="section"><a href="ch16s05.html">16.5. 摄像头识别人脸</a></span></dt></dl></div>
<p>人脸识别，是基于人的脸部特征信息进行身份识别的一种生物识别技术。用照相机、摄像机或摄像头采集含有人脸的图像或视频流，并自动在图像中检测和跟踪人脸，进而对检测到的人脸进行脸部识别的一系列相关技术，通常也叫做人像识别、面部识别。</p>
<a class="ulink" href="https://github.com/ageitgey/face_recognition" target="_top">https://github.com/ageitgey/face_recognition</a>
<p>face_recognition，这个是一个开源的人脸识别库，该模块是基于业内领先的C++开源库dlib中的深度学习模型，用<a class="ulink" href="https://link.zhihu.com/?target=http%3A//vis-www.cs.umass.edu/lfw/" target="_top">Labeled Faces in the Wild</a>人脸数据集进行测试，有高达99.38%的准确率。</p>
<div class="section"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a id="id1202"></a>16.1. 安装</h2></div></div></div>
<p>需要用到 dlib,face_recognition,opencv 这三个模块。</p>
<pre class="screen">
pip3 install dlib
pip3 install opencv-python
pip3 install face_recognition
</pre>
</div>
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