摘 要
近年来，网络技术日新月异，我们已经进入了大数据的时代并成为其中第一员，由此带来数据量的飞跃式的增长，而如何利用这些数据产生实际的生产价值则成为了大数据时代的一个重要问题。而人脸识别技术是一份对大数据价值应用的标准答卷。传统的身份验证系统基于动态的密码和手机认证，这样的方式看似安全，实际上却非常容易产生信息暴露、信息被盗取、信息丢失等问题。假设有一天，用户丢失了手机或者身份证，如果是传统身份验证技术，那么就可能被不法之人所利用，而如果是生物特征识别技术，则不会存在这一问题。因此，人脸识别技术在未来不久也将会成为主要的信息安全技术。人脸识别是这种识别技术的和弦技术之一，同时也是最成熟的技术之一。利用一段视频或者图片，判断是否为本人，就是人脸识别技术所在做的。而人脸识别技术已经被广泛应用于入口检测之中，未来的应用只会越来越广泛。
人脸识别技术随着卷积神经网络的提出进入了飞速发展阶段，越来越多的研究人员通过改变卷积神经网络结构获得来越来越好的性能与效率。与此同时，人脸识别的数据集的数据量也越来越大、数据集的种类也越来越丰富，出现了3D人脸数据集和各种姿态、光照人脸数据集。人脸识别技术在目前已经发展的越来越成熟了，但是在应用过程中，人脸识别技术陷入了隐私泄露和隐私安全的困境，这种现象在国外尤其严重。
本文对基于深度学习的人脸识别做出了研究，同时也应用研究成果设计了拍照签到与网课在线检测系统。本文的人脸识别分为人脸检测、人脸对齐、人脸检验这三个过程。在人脸检测过程中，本文使用了LFFD算法进行快速人脸检测与活体检测以应对网课在线检测这一场景，同时也使用了DSFD算法进行高精度人脸识别以应对拍照签到这一场景。人脸对齐过程中，本文采用的是快速、简便的5关键点对齐。在最后的人脸检验过程中，本文使用了最新的SeetaFace6算法，支持口罩检测。本文也在最后的人脸检验过程中采用GPU加速了人脸距离计算过程，这不同于目前绝大部分人脸识别技术的人脸距离计算方式，非常的快速。
关键词：人脸检测；人脸对齐；人脸识别；卷积神经网络
Abstract
In recent years, network technology has changed rapidly. We have entered the era of big data and become the first member of it, which has brought a leap in data volume growth. How to use these data to generate actual production value has become the era of big data. An important issue. The face recognition technology is a standard answer to the application of big data value. The traditional identity verification system is based on dynamic passwords and mobile phone authentication. This method seems to be safe, but in fact it is very easy to cause problems such as information exposure, information theft, and information loss. Suppose that one day, the user loses the mobile phone or ID card. If it is a traditional authentication technology, it may be used by illegal people. If it is a biometric technology, there will be no such problem. Therefore, face recognition technology will become the main information security technology in the near future. Face recognition is one of the chord technologies of this recognition technology, and it is also one of the most mature technologies. Using a video or a picture to determine whether it is the person is what the face recognition technology is for. While face recognition technology has been widely used in entrance detection, future applications will only become more widespread。
Face recognition technology has entered a stage of rapid development with the introduction of convolutional neural networks. More and more researchers have obtained better and better performance and efficiency by changing the structure of convolutional neural networks. At the same time, the data volume of face recognition data sets is getting larger and larger, and the types of data sets are becoming more and more abundant. 3D face data sets and various poses and lighting face data sets have appeared. Face recognition technology has become more and more mature at present, but in the application process, face recognition technology has fallen into the dilemma of privacy leakage and privacy security, which is especially serious abroad.
This paper researches on face recognition based on deep learning, and also uses the research results to design a photo check-in and online course detection system. Face recognition in this paper is divided into three processes: face detection, face alignment, and face verification. In the face detection process, this paper uses the LFFD algorithm for fast face detection and live detection to deal with the online class online detection scene, and also uses the DSFD algorithm for high-precision face recognition to deal with the scene of taking photos. In the process of face alignment, this article uses a quick and easy 5 key point alignment. In the final face verification process, this article uses the latest SeetaFace6 algorithm to support mask detection. This article also uses GPU to accelerate the face distance calculation process in the final face verification process, which is different from the current face distance calculation method of most face recognition technologies, which is very fast.
Keywords: face detection；face alignment； face recognition； convolutional neural network
目 录
第一章 绪 论 - 1 -
1.1 引言 - 1 -
1.2 人脸识别技术课题背景 - 2 -
1.3人脸识别现状 - 3 -
1.3.1 国外现状 - 3 -
1.3.2 国内现状 - 3 -
1.4 本文的内容结构 - 5 -
第二章 人脸识别与深度学习 - 6 -
2.1深度神经网络基础 - 6 -
2.2卷积神经网络基础 - 7 -
2.3 深度学习的参数更新 - 13 -
2.4本章小结 - 13 -
第三章 人脸检测算法研究 - 14 -
3.1目标检测算法 - 14 -
3.2三种人脸检测算法 - 17 -
3.2.1 DSFD人脸检测算法 - 17 -
3.2.2 LFFD人脸检测算法 - 18 -
3.2.3HOG检测算法 - 20 -
3.3 本章小结 - 22 -
第四章 人脸对齐 - 23 -
4.1 FAN网络与人脸对齐 - 23 -
4.2人脸关键点定位与人脸对齐 - 23 -
4.3活体检测 - 25 -
4.4 本章小结 - 26 -
第五章 人脸识别系统设计 - 27 -
5.1 开发环境 - 27 -
5.2人脸识别方法综述 - 27 -
5.3 dlib与SeetaFace人脸识别 - 28 -
5.4 人脸识别系统设计 - 32 -
5.4.1 教室拍照签到 - 32 -
5.4.2 学生网课在线检测 - 33 -
5.5本章小结 - 34 -
第六章 结论 - 35 -
参考文献 - 36 -
致谢 - 37 -
附录A - 38 -
附录B - 42 -
