人脸识别(英文)Face_Recognition

人脸识别英文专业词汇gallery set参考图像集Probe set=test set测试图像集face renderingFacial Landmark Detection人脸特征点检测3D Morphable Model 3D形变模型AAM (Active Appearance Model)主动外观模型Aging modeling老化建模Aging simulation老化模拟Analysis by synthesis 综合分析Aperture stop孔径光标栏Appearance Feature表观特征Baseline基准系统Benchmarking 确定基准Bidirectional relighting 双向重光照Camera calibration摄像机标定（校正）Cascade of classifiers 级联分类器face detection 人脸检测Facial expression面部表情Depth of field 景深Edgelet 小边特征Eigen light-fields本征光场Eigenface特征脸Exposure time曝光时间Expression editing表情编辑Expression mapping表情映射Partial Expression Ratio Image局部表情比率图(,PERI) extrapersonal variations类间变化Eye localization,眼睛定位face image acquisition 人脸图像获取Face aging人脸老化Face alignment人脸对齐Face categorization人脸分类Frontal faces 正面人脸Face Identification人脸识别Face recognition vendor test人脸识别供应商测试Face tracking人脸跟踪Facial action coding system面部动作编码系统Facial aging面部老化Facial animation parameters脸部动画参数Facial expression analysis人脸表情分析Facial landmark面部特征点Facial Definition Parameters人脸定义参数Field of view视场Focal length焦距Geometric warping几何扭曲Street view街景Head pose estimation头部姿态估计Harmonic reflectances谐波反射Horizontal scaling水平伸缩Identification rate识别率Illumination cone光照锥Inverse rendering逆向绘制技术Iterative closest point迭代最近点Lambertian model朗伯模型Light-field光场Local binary patterns局部二值模式Mechanical vibration机械振动Multi-view videos多视点视频Band selection波段选择Capture systems获取系统Frontal lighting正面光照Open-set identification开集识别Operating point操作点Person detection行人检测Person tracking行人跟踪Photometric stereo光度立体技术Pixellation像素化Pose correction姿态校正Privacy concern隐私关注Privacy policies隐私策略Profile extraction轮廓提取Rigid transformation刚体变换Sequential importance sampling序贯重要性抽样Skin reflectance model,皮肤反射模型Specular reflectance镜面反射Stereo baseline 立体基线Super-resolution超分辨率Facial side-view面部侧视图Texture mapping纹理映射Texture pattern纹理模式Rama Chellappa读博计划：1.完成先前关于指纹细节点统计建模的相关工作。

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()。

Recognition识别speech recognition systems 语音识别系统facial recognition 人脸识别短语：win recognition from sb.赢得(某人的)赞誉，博得(某人的)好评He has won wide recognition in the field of tropical medicine. 他在热带疾患这一医学领域里广获赞誉.Xinhuanet 2011-07-27 15:06标题Chinese synchronized（同步的）swimmers(花样游泳队员，synchronized swimming花样游泳) aim high at London OlympicsWith her(China's Japanese coach) instruction, Chinese swimmers have improved markedly in strength, technique and speed, and gradually won recognition from international judges.From the 2008 Summer Olympics in Beijing to the 2010 World Expo in Shanghai, and from China's rapid economic growth to its successful dealing with the latest global financial crisis, all of those feats won international recognition.短语：beyond recognition面目全非，完全改了模样，变化大得难以认出Local police said that several of the bodies recovered from the bus were burned beyond recognition, adding that they will have to be identified through DNA testing. 背景,北京-珠海的长途客车途经信阳时发生着火.短语：recognition and appreciation 肯定与欣赏承认Recognition helps funding Libyan opposition: U.S. official 2011-07-16 04:27:17 The recognition of Libya's opposition National Transitional Council (NTC) by the U.S. and other countries will enable it to have more funding from Libya's frozen assets, U.S. State Department spokesman Mark Toner said on Friday."The most important thing today is the recognition of the TNC(Transitional National Council) as the legitimate voice of the Libyan people," he （a seniorofficial for the Transitional National Council (TNC) ）sai d. 背景：利比亚问题联络小组15日在土耳其伊斯坦布尔开会，承认利比亚反对派“全国过渡委员会”为“合法政府”Li Guangdou, an analyst on brand competitiveness, said Chinese companies have greatly improved their manufacturing capacity during the past three decades, but they still lag their Western counterparts in brand promotion. It results in some Chinese customers having a higher recognition of foreign brands, even though many Chinese companies can also produce highquality goods," Li said on Wednesday. Li expects that the Da Vinci case will prompt Chinese consumers "to be more rational about foreign brands."背景:达芬奇家居事件。

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 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。

gallery set参考图像集Probe set=test set测试图像集face renderingFacial Landmark Detection人脸特征点检测3D Morphable Model 3D形变模型AAM (Active Appearance Model)主动外观模型Aging modeling老化建模Aging simulation老化模拟Analysis by synthesis 综合分析Aperture stop孔径光标栏Appearance Feature表观特征Baseline基准系统Benchmarking 确定基准Bidirectional relighting双向重光照Camera calibration摄像机标定（校正）Cascade of classifiers级联分类器face detection 人脸检测Facial expression面部表情Depth of field 景深Edgelet 小边特征Eigen light-fields本征光场Eigenface特征脸Exposure time曝光时间Expression editing表情编辑Expression mapping表情映射Partial Expression Ratio Image局部表情比率图(,PERI) extrapersonal variations类间变化Eye localization,眼睛定位face image acquisition人脸图像获取Face aging人脸老化Face alignment人脸对齐Face categorization人脸分类Frontal faces 正面人脸Face Identification人脸识别Face recognition vendor test人脸识别供应商测试Face tracking人脸跟踪Facial action coding system面部动作编码系统Facial aging面部老化Facial animation parameters脸部动画参数Facial expression analysis人脸表情分析Facial landmark面部特征点Facial Definition Parameters人脸定义参数Field of view视场Focal length焦距Geometric warping几何扭曲Street view街景Head pose estimation头部姿态估计Harmonic reflectances谐波反射Horizontal scaling水平伸缩Identification rate识别率Illumination cone光照锥Inverse rendering逆向绘制技术Iterative closest point迭代最近点Lambertian model朗伯模型Light-field光场Local binary patterns局部二值模式Mechanical vibration机械振动Multi-view videos多视点视频Band selection波段选择Capture systems获取系统Frontal lighting正面光照Open-set identification开集识别Operating point操作点Person detection行人检测Person tracking行人跟踪Photometric stereo光度立体技术Pixellation像素化Pose correction姿态校正Privacy concern隐私关注Privacy policies隐私策略Profile extraction轮廓提取Rigid transformation刚体变换Sequential importance sampling序贯重要性抽样Skin reflectance model,皮肤反射模型Specular reflectance镜面反射Stereo baseline立体基线Super-resolution超分辨率Facial side-view面部侧视图Texture mapping纹理映射Texture pattern纹理模式Rama Chellappa读博计划：1.完成先前关于指纹细节点统计建模的相关工作。

人脸识别英文作文I think facial recognition technology is both fascinating and a little bit scary. It's amazing how a computer can analyze and identify a person's face with such accuracy. It's like something out of a sci-fi movie.The idea of being able to unlock your phone or access your bank account just by looking at a camera is pretty cool. It's convenient and feels like the future is already here.On the other hand, the thought of cameras everywhere being able to track and identify us at any time is a little unsettling. It feels like our privacy is being invaded, and it's hard to know who might have access to all that information.I also worry about the potential for misuse of facial recognition technology. It's easy to imagine how it could be used for surveillance or even discrimination. It'simportant to have regulations in place to protect people from these kinds of abuses.At the same time, there are also some really positive uses for facial recognition. For example, it can help law enforcement identify criminals or find missing persons. It can also be used for security in places like airports and government buildings.Overall, I think facial recognition technology has alot of potential, but it's important to proceed with caution. We need to balance the benefits with the potential risks and make sure that people's rights and privacy are protected.。

人脸识别，是基于人的脸部特征信息进展身份识别的一种生物识别技术。

用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展脸部的一系列相关技术，通常也叫做人像识别、面部识别。

中文名人脸识别别名人像识别、面部识别工具摄像机或摄像头传统技术可见光图像的人脸识别处理方法人脸识别算法用途身份识别1技术特点2技术流程▪人脸图像采集及检测▪人脸图像预处理▪人脸图像特征提取▪人脸图像匹配与识别3识别算法4识别数据5配合程度6优势困难▪优势▪困难7主要用途8应用前景9主要产品▪数码相机▪门禁系统▪身份辨识▪网络应用▪娱乐应用10应用例如技术特点编辑人脸识别传统的人脸识别技术主要是基于可见光图像的人脸识别，这也是人们熟悉的识别方式，已有30多年的研发历史。

但这种方式有着难以克制的缺陷，尤其在环境光照发生变化时，识别效果会急剧下降，无法满足实际系统的需要。

解决光照问题的方案有三维图像人脸识别，和热成像人脸识别。

但这两种技术还远不成熟，识别效果不尽人意。

迅速开展起来的一种解决方案是基于主动近红外图像的多光源人脸识别技术。

它可以克制光线变化的影响，已经取得了卓越的识别性能，在精度、稳定性和速度方面的整体系统性能超过三维图像人脸识别。

这项技术在近两三年开展迅速，使人脸识别技术逐渐走向实用化。

人脸与人体的其它生物特征〔指纹、虹膜等〕一样与生俱来，它的唯一性和不易被复制的良好特性为身份鉴别提供了必要的前提，与其它类型的生物识别比拟人脸识别具有如下特点：非强制性：用户不需要专门配合人脸采集设备，几乎可以在无意识的状态下就可获取人脸图像，这样的取样方式没有“强制性〞；非接触性：用户不需要和设备直接接触就能获取人脸图像；并发性：在实际应用场景下可以进展多个人脸的分拣、判断及识别；除此之外，还符合视觉特性：“以貌识人〞的特性，以及操作简单、结果直观、隐蔽性好等特点。

技术流程编辑人脸识别系统主要包括四个组成局部，分别为：人脸图像采集及检测、人脸图像预处理、人脸图像特征提取以及匹配与识别。

Face Recognition: A Double-Edged Sword ofModern TechnologyIn the age of digital transformation, face recognition technology has become a ubiquitous part of our daily lives. From unlocking smartphones to accessing secure areas, this cutting-edge technology has revolutionized the way we interact with the world. However, as with any technology, face recognition presents both remarkable benefits and significant concerns.The primary benefit of face recognition is its convenience and efficiency. Gone are the days of fumbling with keys or forgetting passwords. With a simple glance at a camera, individuals can gain access to their devices or buildings with ease. This便利has streamlined numerous processes, from airport security checks to retail payments, significantly improving the user experience.Moreover, face recognition has immense potential in law enforcement and national security. It can assist in identifying criminals, tracking fugitives, and even preventing crimes by recognizing suspicious activities. Thetechnology has been used successfully in various countries to solve crimes and keep the public safe.However, the rise of face recognition technology also raises serious privacy concerns. In a world where every face can be captured and identified, the potential for misuse and abuse is alarming. Governments and corporations could potentially misuse this data for surveillance, stalking, or even discrimination. Furthermore, the storage and protection of this sensitive information aresignificant challenges that need to be addressed.Moreover, the accuracy of face recognition technologyis not without flaws. While it can be highly accurate in ideal conditions, factors such as lighting, angles, and even facial hair can affect recognition rates. This can lead to false positives or negatives, potentially resulting in embarrassing misidentifications or even serious consequences such as wrongful arrests.Additionally, the ethical implications of face recognition are profound. The technology has the potential to create a divide between those who are recognized and those who are not. This could lead to discriminationagainst certain groups, such as minorities or individuals with disabilities, who may be more difficult to identify. In conclusion, face recognition technology is a powerful tool that has brought remarkable benefits to our lives. However, it also poses significant challenges and concerns that need to be addressed. As we continue to embrace this technology, it is crucial that we do so with a balanced perspective, ensuring that its benefits are maximized while minimizing its negative impacts on privacy, security, and equality.**人脸识别：现代科技的双刃剑**在数字化转型的时代，人脸识别技术已经成为我们日常生活中无处不在的一部分。

人脸识别概述及识别的基本方法与流程论坛里面经常有人问关于识别，特别是关于人脸识别的一些事情，今天特吧人脸识别的基本概念，以及大概步骤给大家介绍一下，让大家对人脸识别有一个综合的认识。

（信息整理收集于网上）人脸识别(Face Recognition)是一种依据人的面部特征(如统计或几何特征等)，自动进行身份鉴别蝗一种技术，它综合运用了数字图像/视频处理、模式识别等多种技术。

1、人脸识别技术概念人脸识别(Face Recognition)是一种依据人的面部特征(如统计或几何特征等)，自动进行身份鉴别蝗一种技术，它综合运用了数字图像/视频处理、模式识别等多种技术。

广义上的人脸识虽指对人体脸部的识别，特指脸部眼、鼻、口以及面颊等部位的识别。

人脸识别技术又称为面像识别。

人脸识别技术又称为面像识别、人像识别、相貌识别、面孔识别、面部识别等。

这些叫法的含义有细微的差别，特定意义上的人脸识别一般依据视频中活体人脸进行身份识别，比如门禁等应用，而面像识别、人像识别则强调的是像，以确定人像图片中人物的身份为主，比如照片、画像比对等应用。

人脸识别主要依据人脸上的特征，依据那些在不同个体之间存在较大差异而对于同一个人比较稳定的度量。

由于人脸变化复杂，因此特征表述和特征提取十分困难。

诸多因素使人脸识别成为一项极富挑战性的课题。

2、人脸识别技术三个主要环节人脸检测(Face Detection):实现人脸自动提取采集，从摄像机视野或图片内的复杂背景图像中自动提取人的面部图像。

确认检测目标的人脸属性。

人脸确认(Face Verification):将某人面像与指定人员面像进行一对一的比对，根据其相似程度(一般以是否达到或超过某一量化的可信度指标/阀值为依据)来判断二者是否是同一人。

人脸鉴别(Face Identification)：将某人面像与数据库中的多人的人脸进行比对(有时也称“一对多”比对)，并根据比对结果来鉴定此人身份，或找到其中最相似的人脸，并按相似程度的大小输出检索结果。

摘要摘要随着社会的发展，各个方面对快速有效的自动身份验证的要求日益迫切。

由于生物特征是人的内在属性，具有很强的自身稳定性和个体差异性，因此是身份验证的理想依据。

这其中，利用人脸特征又是最自然直接的手段，相比其他生物特征，它具有直接、友好、方便的特点，易于为用户接受。

人脸识别是一个涉及面广且又很有挑战性的研究课题，近年来关于人脸识别的研究取得了较大的进展。

关键词：人脸识别，AT89C51单片机，液晶显示器AbstractAs the development of the society, there are increasing demands in automatic identity check. Since some biological characteristics are intrinsic and stable to people and are strongly different from one to the others, they can be used as features for identity check. Among all the characteristics of human, the characteristics of face are the most direct tools which are friendly and convenient and can easily be accepted by the customers.Face recognition is an extensive and challenging research problem. Recently, significant progresses have been made in the technology of the face recognition.Key word:AT89C51 MCU，human face recognition，LCD目录摘要 (Ⅰ)Abstract (Ⅱ)第1章绪论 (3)1.1人脸识别系统的背景和意义 (3)1.2国内外人脸识别系统的研究现状 (4)1.2.1国外的发展概况 (4)1.2.2 国内的发展概况 (5)1.3 本论文的内容 (5)1.4 本文的任务 (5)第2章人脸图片识别总体方案设计 (6)2.1系统硬件结构 (6)第3章系统硬件部分的设计与实现 (7)3. 1硬件设计基本流程 (7)2.2单片机的发展概况及其选择 (8)3.2 AT89C51单片机的介绍 (8)3.2.1 AT89C51单片机的特点 (14)3.2.2 AT89C51单片机的硬件结构 (15)3.3 图片的导入 (15)3.3.1 MAX232资料简介 (16)3.4显示器的选择 (18)3.5.1 12864液晶介绍 (18)3.6 EPROM和RAM的综合扩展 (32)3.6.1 62256 RAM芯片介绍 (33)3.6.2 27256 EPROM芯片介绍 (34)3.6.3 74LS373 锁存器原理 (36)第4章系统可靠性的设计 (40)4.1 硬件可靠性的设计 (40)4.2 本系统中的抗干扰的预防措施 (40)致谢 (42)参考文献 (43)第1章绪论1.1人脸识别系统的背景和意义鉴别人的身份是一个非常困难的问题，传统的身份鉴别方法把这个问题转化为鉴别一些标识个人身份的事物，这包括两个方面：①身份标识物品，比如钥匙、证件、ATM卡等；②身份标识知识，比如用户名和密码。

FaceRecognition一、定义1.人脸识别特指利用分析比较人脸视觉特征信息进行身份鉴别的计算机技术。

广义的人脸识别实际包括构建人脸识别系统的一系列相关技术，包括人脸图像采集、人脸定位、人脸识别预处理、身份确认以及身份查找等；而狭义的人脸识别特指通过人脸进行身份确认或者身份查找的技术或系统。

人脸识别是一项热门的计算机技术研究领域，它属于生物特征识别技术，是对生物体（一般特指人）本身的生物特征来区分生物体个体。

2.LFWLabeled Faces in the Wild （户外脸部监测数据库）是人脸识别研究领域比较有名的人脸图像集合，其图像采集自Yahoo! News，共13233幅图像，其中5749个人，其中1680人有两幅及以上的图像，4069人只有一幅图像；大多数图像都是由Viola-Jones人脸检测器得到之后，被裁剪为固定大小，有少量的人为地从false positive 中得到。

所有图像均产生于现实场景（有别于实验室场景），具备自然的光线，表情，姿势和遮挡，且涉及人物多为公物人物，这将带来化妆，聚光灯等更加复杂的干扰因素。

因此，在该数据集上验证的人脸识别算法，理论上更贴近现实应用，这也给研究人员带来巨大的挑战。

3.FDDBFDDB全称Face Detection Data Set and Benchmark，是由马萨诸塞大学计算机系维护的一套公开数据库，为来自全世界的研究者提供一个标准的人脸检测评测平台，其中涵盖在自然环境下的各种姿态的人脸，作为全世界最具权威的人脸检测评测平台之一，FDDB使用Faces in the Wild数据库中的包含5171张人脸的2845张图片作为测试集，而其公布的评测集也代表了人脸检测的世界最高水平。

4.300-w人脸关键点定位5.FRVTFace Recognition Vendor Test人脸识别供应商测试，由美国国家标准技术研究所定制。

更趋近于现实应用的人脸识别测试。

人工智能英语专业词汇人工智能（ArtificialIntelligence）是近年来备受关注的一个热门领域。

它涉及到许多领域，如机器学习（Machine Learning）、自然语言处理（Natural Language Processing）、计算机视觉（Computer Vision）等等。

在这些领域中，有许多专业术语。

下面我们来了解一些人工智能英语专业词汇。

机器学习（Machine Learning）是人工智能领域中的一个重要分支。

它指的是利用统计学方法使计算机自动学习并不断改进的能力。

这种能力可以应用于各种领域，如推荐系统（Recommendation System）、图像识别（Image Recognition）、自然语言处理等。

自然语言处理（Natural Language Processing）指的是计算机与人类语言的交互。

它包括语音识别（Speech Recognition）、自动翻译（Machine Translation）、情感分析（Sentiment Analysis）等。

自然语言处理的目标是让计算机能够理解和处理人类语言，从而更好地服务于人类。

计算机视觉（Computer Vision）是指让计算机能够自动理解和分析图像和视频，从而实现各种应用。

这些应用包括人脸识别（Facial Recognition）、自动驾驶（Autonomous Driving）、医学成像（Medical Imaging）等。

深度学习（Deep Learning）是一种机器学习的方法，它使用多层神经网络（Neural Networks）来解决复杂的问题。

深度学习在计算机视觉、自然语言处理等领域中取得了很多成果。

神经网络（Neural Networks）是一种基于生物神经元的计算模型。

它是深度学习的核心部分，用于学习和处理复杂的数据。

神经网络可以通过不断地训练来不断地优化自己的性能。

总之，人工智能英语专业词汇是人工智能领域中不可或缺的一部分。

FaceRecognition，不只是⼈脸识别⽬录：⼀、face_recognition是什么⼆、如何安装三、原理四、演⽰五、⼿写简单的神经⽹络⼀、face_recognition是什么1. face_recognition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。

2. 基于业内领先的C++开源库 dlib中的深度学习模型，⽤Labeled Faces in the Wild⼈脸数据集进⾏测试，有⾼达99.38%的准确率。

⼆、如何安装Linux下配置face_recognition具体详情参考我的博客1、如linux下已有python2.7,但需要更新⼀下python 2.7⾄python2.xsudo add-apt-repository ppa:fkrull/deadsnakes-python2.7sudo apt-get updatesudo apt-get upgrade2、部署步骤安装Boost, Boost.Pythonsudo apt-get install build-essential cmakesudo apt-get install libgtk-3-devsudo apt-get install libboost-all-devInstallation of Cmake:（it tooks a while to install ~1.5 min）1 sudo wget https:///files/v3.9/cmake-3.9.0-rc5.tar.gz -O cmake.tar.gz2 sudo tar -xvf cmake.tar.gz3 cd cmake-3.9.0-rc5/4 sudo chmod +x bootstrap5 sudo ./bootstrap6 sudo make7 sudo make install注：安装好cmake后，输⼊cmake -version查看cmake版本是否安装成功。