face recognition,人脸识别

“人脸识别系统”英语怎么说名词解释：人脸识别系统以人脸识别技术为核心，是一项新兴的生物识别技术，是当今国际科技领域攻关的高精尖技术。

它广泛采用区域特征分析算法，融合了计算机图像处理技术与生物统计学原理于一体，利用计算机图像处理技术从视频中提取人像特征点，利用生物统计学的原理进行分析建立数学模型，具有广阔的发展前景。

你知道怎么用英语表达吗?The Beijing-Shanghai high-speed railway plans to install a face recognition system to catch fugitives, China National Radio (CNR) reported Sunday.Three key railway stations including Shanghai Hongqiao station, Tianjin west station and Ji'nan west station in Shandong Province are expected to install the system, which helped Beijing police apprehend around 100 suspects in a month after two Beijing railway stations adopted it in 2006, the report said.据中央人民广播电台周日报道，京沪高铁将计划安装人脸识别系统，以抓捕逃犯。

包括上海虹桥火车站、天津西站以及位于山东省的济南西站在内的三个重点车站将预计安装这一系统。

据报道，北京的两个火车站在2006年已采用了这套系统，在仅仅一个月内就借此帮助北京警方逮捕了约100名嫌疑犯。

【讲解】：人脸识别系统的英语表达是：face recognition system，另外我们也可以说成facial recognition system。

face_recognition⼈脸识别模块face_recognition ⼈脸识别模块（安装之前，必须先安装dlib库）1. 基于dlib库，进⾏了⼆次封装。

号称是世界上最简洁的⼈脸识别库。

2. 训练数据集：是⿇省理⼯⼤学下属的学院利⽤13000多张照⽚为基础。

主要是以欧美⼈为主。

在 command line下安装模块时：F:\Python_AI\venv\Scripts> pip install face_recoginitonload_image_file : 加载要识别的⼈脸图像，返回Numpy数组，返回的是图⽚像素的特征向量（⼤⼩，⽅向）face_loctions: 定位图中⼈脸的坐标位置。

返回矩形框的location（top,right,bottom,left）face_landmarks: 识别⼈脸的特征点; 返回68个特征点的坐标位置（chin....）face_encodings: 获取图像中所有⼈脸的编码信息（向量）compare_faces: 由⾯部编码信息进⾏⾯部识别匹配。

#!/usr/bin/env python# !_*_ coding:utf-8 _*_import face_recognitionfrom PIL import Image, ImageDrawimport cv2# face_image = face_recognition.load_image_file('imgs/twis001.jpg')face_image = cv2.imread('imgs/twis001.jpg')face_marks_list = face_recognition.face_landmarks(face_image)pil_image = Image.fromarray(face_image)d = ImageDraw.Draw(pil_image)for face_marks in face_marks_list:facial_features = ['chin','left_eyebrow','right_eyebrow','nose_bridge','nose_tip','left_eye','right_eye','bottom_lip']for facial_feature in facial_features:# print("{}: 特征位置：{}".format(facial_feature, face_marks[facial_feature]))# d.line(face_marks[facial_feature], fill=(0, 255, 0), width=5)# d.point(face_marks[facial_feature], fill=(255, 0, 0))for p in face_marks[facial_feature]:print(p)cv2.circle(face_image, p, 1, (0, 255, 0), 1)cv2.imshow("images", face_image)cv2.imwrite("twis01.jpg",face_image)cv2.waitKey(0)cv2.destroyAllWindows()# pil_image.show()#!/usr/bin/env python# !_*_ coding:utf-8 _*_import face_recognitionfrom PIL import Image, ImageDraw, ImageFontimage_known = face_recognition.load_image_file('imgs/yangmi/yangmidanr00.jpg')image_unknown = face_recognition.load_image_file('imgs/yangmi/yangmihe02.jpg')image_known_encodings = face_recognition.face_encodings(image_known)[0]face_locations = face_recognition.face_locations(image_unknown)results = []for i in range(len(face_locations)):top, right, bottom, left = face_locations[i]face_image = image_unknown[top:bottom, left:right]face_encoding = face_recognition.face_encodings(face_image)if face_encoding:result = {}matches = face_pare_faces(face_encoding, image_known_encodings, tolerance=0.5) if True in matches:print('在未知图⽚中找到了已知⾯孔')result['face_encoding'] = face_encodingresult['is_view'] = Trueresult['location'] = face_locations[i]result['face_id'] = i + 1results.append(result)if result['is_view']:print("已知⾯孔匹配照⽚上的第{}张脸！".format(result['face_id']))pil_image = Image.fromarray(image_unknown)draw = ImageDraw.Draw(pil_image)view_face_locations = [i['location'] for i in results if i['is_view']]for location in view_face_locations:top, right, bottom, left = locationdraw.rectangle([(left, top), (right, bottom)], outline=(0, 255, 0), width=2)font = ImageFont.truetype("consola.ttf", 20, encoding='unic')draw.text((left, top - 20), "yangmi", (255, 0, 0), font=font)pil_image.show()# 可以试着⽤cv2来画框，和写字 puttext#!/usr/bin/env python# !_*_ coding:utf-8 _*_import face_recognitionimport cv2img_known = face_recognition.load_image_file("imgs/joedan/cows.jpeg")img_unkown = face_recognition.load_image_file("imgs/joedan/joedan01.jpg")face_encodings_known = face_recognition.face_encodings(img_known)face_encodings_unknow = face_recognition.face_encodings(img_unkown)[0]matches = face_pare_faces(face_encodings_known, face_encodings_unknow, tolerance=0.5) print(matches)locations = face_recognition.face_locations(img_known)print(locations)if True in matches:index = matches.index(True)match = locations[index]print(match)top, right, bottom, left = matchcv2.rectangle(img_known, (left, top), (right, bottom), (0, 0, 255), 2)cv2.imshow("images", img_known)cv2.waitKey(0)cv2.destroyAllWindows()。

face_recognition库原理face_recognition是一个基于深度学习的人脸识别库，由Adam Geitgey开发。

它使用了dlib库中的深度学习模型，通过对图像中的人脸进行编码，从而实现了高精度的人脸识别。

face_recognition库的原理可以分为以下几个关键步骤：1. 人脸检测：face_recognition首先使用dlib库中的人脸检测器，检测图像中的人脸位置。

该检测器使用了一个基于梯度的人脸检测算法，可以准确地定位图像中的人脸。

2. 人脸对齐：在进行人脸识别之前，需要将检测到的人脸进行对齐，使得它们具有相似的姿态和大小。

face_recognition使用了基于人脸关键点的对齐方法，通过检测人脸中的关键点，如眼睛、鼻子和嘴巴等，将人脸对齐到一个统一的标准。

3. 人脸编码：对于每个对齐后的人脸，face_recognition使用一个深度卷积神经网络（Convolutional Neural Network，CNN）对其进行编码。

这个CNN模型是在大量人脸图像上进行训练得到的，可以将每个人脸编码为一个128维的向量。

这个编码向量被认为是人脸的“特征”，具有较强的表征能力。

4. 人脸比对：在进行人脸识别时，face_recognition将两个人脸的编码向量进行比对，通过计算它们之间的欧氏距离来评估它们的相似度。

如果两个人脸的编码向量距离较近，则认为它们属于同一个人；反之，如果距离较远，则认为它们属于不同的人。

5. 人脸识别：基于人脸比对的结果，face_recognition可以实现人脸识别功能。

给定一个待识别的人脸图像，它可以与已知的人脸进行比对，从而判断出这个人脸属于哪个已知的人。

通常，我们可以将已知人脸的编码向量保存在数据库中，每次进行识别时，都需要将待识别的人脸与数据库中的人脸进行比对，找到最相似的人脸。

总结起来，face_recognition库的原理是通过人脸检测、人脸对齐、人脸编码和人脸比对等步骤，实现了高精度的人脸识别。

Face RecognitionToday ,I will talk about the study about face recognition.(第二页)As for the face recognition, we main talk about Two-Dimensional Techniques. The study is from The University of York ,Department of Computer Science , as for the date, it is September 2005.(第三页)We say the background.The current identification technology mainly include: fingerprint identification指纹识别, retina recognition视网膜识别, iris recognition虹膜识别, gait recognition步态识别, vein recognition静脉识别, face recognition人脸识别, etc.advantages优点：Compared with other identification methods, face recognition because of its direct, friendly and convenient features, users do not have any psychological barriers, is easy to be accepted by users.(第四页)Two-Dimensional Face Recognition is main about Face Localization.This consists of two stages: face detection（人脸检测）and eye localization（眼睛定位）. (第五页)Today we main study the research of eye localization.Eye localization is performed on the set of training images, which is then separated into two groups. By it, we can compute the average distance from the eye template. one is eye detection was successful (like the picture on), the dark picture means the detected eyes is closed to the eye template; and the other is failed(like the picture down), the bright points down means doesn’t close.(第六页)We do the research using the way: The Direct Correlation Approach（直接相关方法）.This is the way we make the study, you can have a little know about it. So I will not talk much about it.(第七页)This is the study’s main Experimental Process.It is divided into some groups, calculate the distance d, between two facial image vectors, we can get an indication of similarity. Then a threshold is used to make the final verification decision.(第八页)The result wo get the picture. By the picture, we gets an EER (能效比)of 25.1%, this means that one quarter of all verification operations carried out resulted in an incorrect classification. That also means Tiny changes cause the change of the location in image.(第九页)Conclusion: Two-Dimensional Techniques (we say 2D) is an important part in face recognition. It make a large use in face recognition. All in all, Face recognition is the easiest way to be accepted in the identification field.Thank you!。

face_recognition原理Face recognition（人脸识别）是一种基于人脸生物特征的自动识别技术，通过比对和匹配人脸图像中的特征点来确定一个人的身份。

它已经广泛应用于人脸解锁、安全监控、身份验证、自动化检测等众多领域。

Face recognition 原理分为两个主要步骤：人脸检测和人脸特征抽取与匹配。

人脸检测是在图像中找到人脸的位置。

传统方法使用基于特征的方法，比如Haar-like特征和HOG特征。

这些方法通过计算图像中一些特征的数值来判断是否存在人脸。

近年来，深度学习方法也开始广泛应用，使用卷积神经网络（CNN）进行人脸检测。

CNN通过多层卷积操作提取图像中的特征，使用分类器判断图像中是否存在人脸。

人脸特征抽取与匹配是通过对人脸图像进行特征提取，然后进行对比和匹配来确定人脸身份。

传统的方法使用的特征是基于形状或纹理的，如主成分分析（PCA）、线性判别分析（LDA）等。

这些方法通过计算人脸图像的主要特征或者采用分类方法来进行人脸识别。

然而，这些方法受到光照、姿态和遮挡的影响，导致准确度较低。

近年来，深度学习的方法应用起来更加普遍，特别是卷积神经网络（CNN）。

CNN通过多层卷积和池化操作提取人脸图像的特征，并通过全连接层得到中间向量表示。

这些表示被用于判断两个人脸图像是否来自同一个人。

在人脸图像匹配中，通常使用欧氏距离或余弦相似性来度量两个人脸图像之间的差异。

通过计算特征向量之间的相似度得到一个分数。

如果分数超过一个预定义的阈值，则认为两个人脸来自同一个人。

然而，人脸识别仍然面临一些挑战。

首先，对光照、姿态和表情的变化比较敏感，这会导致人脸匹配的降低。

其次，存在一种称为“非重复记录”的攻击，攻击者试图在不同情况下以不同的身份进行注册。

此外，人脸识别还需要解决一些伦理和隐私问题，包括人脸图像的合法获取以及应用程序的合规性。

为了克服这些挑战，研究人员正在不断改进人脸识别技术。

Face recognition is a double-edged swordFace recognition technology has become increasingly popular in recent years. It is a method of identifying individuals based on their facial features. With advancements in artificial intelligence, face recognition has become a powerful tool in various industries such as security, marketing, and law enforcement. One of the most common applications of face recognition technology is in security systems. Security cameras equipped with face recognition technology can identify individuals as they enter a building or access a restricted area. This technology can help prevent unauthorized access and reduce the risk of theft and other criminal activities.In marketing, face recognition technology is used to analyze consumer behavior. By analyzing the facial expressions and emotions of customers, businesses can gain valuable insights into their preferences and buying habits. This information can then be used to create targeted marketing campaigns that are more likely to resonate with the target audience.In law enforcement, face recognition technology is used to identify suspects and missing persons. Police departments use facial recognition technology to match photos of suspects to databases of known criminals or missing persons. This technology can help solve crimes and reunite missing persons with their families.Despite the many benefits of face recognition technology, there are also concerns about privacy and potential misuse of the technology. Critics arguethat face recognition technology could be used to monitor individuals without their knowledge or consent. There is also the potential for the technology to be used to discriminate against certain groups of people.In conclusion, face recognition technology has the potential to revolutionize various industries and improve security measures. However, it is important to consider the potential risks and ethical implications of the technology. As we continue to develop and implement this technology, it is crucial to ensure that it is used in a responsible and ethical manner.译文：人脸识别是一把双刃剑近年来，人脸识别技术越来越流行。

Python使⽤face_recognition⼈脸识别Python 使⽤ face_recognition ⼈脸识别官⽅说明：⼈脸识别 face_recognition 是世界上最简单的⼈脸识别库。

使⽤ dlib 最先进的⼈脸识别功能构建建⽴深度学习，该模型准确率在99.38%。

Python模块的使⽤ Python可以安装导⼊ face_recognition 模块轻松操作，对于简单的⼏⾏代码来讲，再简单不过了。

Python操作 face_recognition API ⽂档：⾃动查找图⽚中的所有⾯部import face_recognitionimage = face_recognition.load_image_file("my_picture.jpg")face_locations = face_recognition.face_locations(image)# face_locations is now an array listing the co-ordinates of each face!还可以选择更准确的给予深度学习的⼈脸检测模型import face_recognitionimage = face_recognition.load_image_file("my_picture.jpg")face_locations = face_recognition.face_locations(image, model="cnn")# face_locations is now an array listing the co-ordinates of each face!⾃动定位图像中⼈物的⾯部特征import face_recognitionimage = face_recognition.load_image_file("my_picture.jpg")face_landmarks_list = face_recognition.face_landmarks(image)# face_landmarks_list is now an array with the locations of each facial feature in each face.# face_landmarks_list[0]['left_eye'] would be the location and outline of the first person's left eye.识别图像中的⾯部并识别它们是谁import face_recognitionpicture_of_me = face_recognition.load_image_file("me.jpg")my_face_encoding = face_recognition.face_encodings(picture_of_me)[0]# my_face_encoding now contains a universal 'encoding' of my facial features that can be compared to any other picture of a face! unknown_picture = face_recognition.load_image_file("unknown.jpg")unknown_face_encoding = face_recognition.face_encodings(unknown_picture)[0]# Now we can see the two face encodings are of the same person with `compare_faces`!results = face_pare_faces([my_face_encoding], unknown_face_encoding)if results[0] == True:print("It's a picture of me!")else:print("It's not a picture of me!")face_recognition ⽤法要在项⽬中使⽤⾯部识别，⾸先导⼊⾯部识别库，没有则安装：import face_recognition基本思路是⾸先加載圖⽚：# 导⼊⼈脸识别库import face_recognition# 加载图⽚image = face_recognition.load_image_file("1.jpg")上⾯这⼀步会将图像加载到 numpy 数组中，如果已经有⼀个 numpy 数组图像则可以跳过此步骤。

人脸识别过程一般要经过图像预处理、特征提取、匹配识别三个步骤,其中特征提取和匹配识别尤为重要。

特征提取的主要工作是从人脸图像中提取一组反应人脸特征的向量表征人脸样本,这个过程常与特征降维重合;匹配识别则是对待识别的图像进行识别分类,这一过程通过选择适当的匹配策略将输入到分类器的图像数据与数据库中的数据进行匹配,进而判断个体身份人脸携带了许多信息,是由眼睛、鼻子、嘴、和下巴等部位组成的,但因个体差异使这些器官的形状和大小及分布因人而异;在人脸图像中, 再加上光照、姿态、表情等因素的变化,使得人脸图像的识别过程变得异常复杂人脸识别的优点——无直接接触性、自然容易接受及非侵犯性等(1)有很好的隐蔽性,不一定需要用户的配合就能釆集到所需画面,在安全监控、疑犯追踪和监控等领域展现了很好的适用性,这是其它的生物特征识别所不能取代的。

(2)它的采集方式友好,是一种非接触式采集,容易被用户接受,不会造成反感及抵触。

(3)它操作起来比较简单,不需要高深的专业知识,便于人们使用,是一种快捷的识别方式。

(4)它具有强大的事后追踪能力,相对于指纹、虹膜等识别能力,普通用户就可以做出判断;(5)识别结果显而易见,更符合人类的认知习惯,具备指纹识别等不具备可交互性,适合于改善人机界面;(6)需要的设备简单、成本低、通用,普通的摄像头就可以达到识别要求强调了其局限性,正是这些局限性给研究人员提供了研究思路。

这些局限性一般包括人脸图像的内在因素和外在因素造成的障碍,内在因素如年龄、表情、面部装饰和种族、性别的不同,外在因素有光照、姿态、摄像机的成像参数及釆集数据的规模等叙述了人脸识别的原理及发展历程,详细介绍了各历程中涌现出的代表性的人脸识别方法,从早期的面部剪影曲线的结构特征提取与分析到特征脸方法的提出,从线性子空间判别分析、统计表观模型、统计模式识别方法到非线性建模方法、统计学习理论、基于Boosting的学习技术、基于三维模型的人脸建模与识别方法。

Github开源⼈脸识别项⽬face_recognitionGithub开源⼈脸识别项⽬face_recognition译者注：本项⽬是⼀个强⼤、简单、易上⼿的⼈脸识别开源项⽬，并且配备了完整的开发⽂档和应⽤案例，特别是兼容树莓派系统。

为了便于中国开发者研究学习⼈脸识别、贡献代码，我将本项⽬README⽂件翻译成。

向本项⽬的所有贡献者致敬。

英译汉：同济⼤学开源软件协会Translator's note:is a powerful, simple and easy-to-use face recognition open source project with complete development documents andapplication cases, especially it is compatible with Raspberry Pi.In order to facilitate Chinese software developers to learn, make progress in face recognition development and source codecontributions, I translated README file into simplified Chinese.Salute to all contributors to this project.Translator: Tommy in Tongji Univerisity Opensource Association本项⽬是世界上最简洁的⼈脸识别库，你可以使⽤Python和命令⾏⼯具提取、识别、操作⼈脸。

本项⽬的⼈脸识别是基于业内领先的C++开源库中的深度学习模型，⽤⼈脸数据集进⾏测试，有⾼达99.38%的准确率。

但对⼩孩和亚洲⼈脸的识别准确率尚待提升。

是美国⿇省⼤学安姆斯特分校（University of Massachusetts Amherst)制作的⼈脸数据集，该数据集包含了从⽹络收集的13,000多张⾯部图像。