Facial Recognition--人脸识别
face_recognition人脸识别模块
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()。
facial recognition阅读理解
facial recognition阅读理解一、简介Facial Recognition是一种基于人工智能的技术，能够识别和分析图像中的面孔，从而对目标对象进行身份验证和定位。
在本文中，我们将讨论这项技术的基本概念、应用场景、优缺点以及相关法律法规。
二、基本原理Facial Recognition的核心原理是基于图像处理和机器学习算法。
它首先对输入的图像进行预处理，如光线调整、面部对齐等，以改善识别精度。
然后，使用机器学习算法训练一个模型，该模型能够从图像中提取面部的特征，并与数据库中的已知面孔进行比对。
如果匹配成功，则识别成功。
三、应用场景1. 安全监控：在公共场所，如机场、车站、大型活动现场等，使用Facial Recognition技术可以进行人员身份验证和流量控制，提高安全保障。
2. 人脸识别门禁：在住宅小区、办公大楼等场所，Facial Recognition门禁系统可以方便快捷地验证访客身份，提高门禁管理效率。
3. 社交媒体应用：许多社交媒体应用使用Facial Recognition技术来识别用户上传的照片中的人物，并提供相关标签和推荐。
4. 执法机构：在刑事调查和人口统计中，Facial Recognition也发挥着重要作用。
四、优缺点优点：1. 准确度高：随着技术的进步，Facial Recognition的准确度越来越高。
2. 快速高效：相对于传统的人工比对方法，Facial Recognition可以大大提高识别的速度和效率。
3. 非接触式：Facial Recognition系统通常是非接触式的，对用户来说更加方便和舒适。
缺点：1. 隐私问题：Facial Recognition涉及个人隐私，需要遵守相关的法律法规。
2. 技术局限性：Facial Recognition技术在一些特殊情况下（如化妆、发型改变、面部遮挡等）可能无法正常工作。
3. 安全风险：如果系统被黑客攻击或滥用来识别无辜公民，可能会引发安全风险。
【中考英语总复习 考点专项练习】39 阅读理解之说明文(原卷及答案)
专项39 阅读理解之说明文＂英语说明文＂，顾名思义，就是一种以＂说明、解释＂为主要表达方式的英语文体。
它是对客观事物的性状、特点、功能和用途等作科学解说的。
它既不像故事那样重在情节的叙述和描写，也不像议论文那样，重在阐明主张和论点论据；更不像科幻作品那样富于想象和虚构夸张。
说明文是通过解说事物、阐明事理，使人们增长知识和技能。
说明文是高考英语阅读理解题中的重点内容之一。
说明文具有与自己特点相适应的说明方法，因此说明文结构复杂，专业术语多，易于拉开考生分数档次，便于高校分层次选拔人才。
然而对于考生来说说明文抽象度高，解题难度大。
高考对说明文的考查多为科普知识，动植物特性、自然现象和新产品、新工艺介绍以及人文地理、风土人情等方面的说明文，文中解释性、定义性、说明性的句子居多。
因此考生要掌握说明文的命题特点，叙述方式，以冷静的心态阅读原文，重点突破长句结构特点和逻辑关系，以便对其做出准确的语意理解。
说明文阅读理解的特征一般说来，英语说明文与其他文体一样，文章所涉及的内容不外乎以下几个方面，即who → what → when → where → how → why。
1. who：问的是这篇文章的主体是谁？（即所要说明和描述的人或事物）2. what：问的是主体做了什么事情？（即主体表现出的特性、功能和用途）3. when和where：是在何时何地发生的？（即何时何地所表现出的特性、功能和用途）4. how：通过什么方式表现出来的？5. why：这种特性功能用途的原因是什么？做说明文阅读理解题的时候，一定要记住上面的wh-word。
边阅读，边搜记，牢记要点，把握全文。
A（2022·西藏·中考真题）The Fuxing train has started running in southwest China’s Tibet along the Lhasa—Nyingchi Railway(拉萨—林芝铁路).The 435-kilometer railway links Lhasa and Nyingchi. It is China’s first electrified(电气化的) railroad on the “Roof of the World”. It shortens the journey between Lhasa and Nyingchi fromover five hours to about 3.5 hours.The new railway covers an area of 226,000 square kilometers. It goes through 47 tunnels (隧道) and crosses 121 bridges. The train has a top speed of 160 kilometers per hour. It is the fastest train on the high land, but it’s a bit slower than some other Fuxing trains in our country because of the special environment. The railway not only makes local people’s life easier, but also brings more tourists to the area.“The railway trip is so relaxing and fast. The new train has brought our family members closer than ever before,” said a teacher who lives in Nyingchi. She used to drive more than five hours from Nyingchi to Lhasa to visit her parents during the summer vacation.Besides carrying passengers, the railway also provides good chances for farmers to sell local products to more places. It’s sure to put new energy into Tibet’s economic(经济的) development. 1．How many hours does the journey take between Lhasa and Nyingchi by Fuxing train? A．About 5 hours.B．About 4 hours.C．About 3.5 hours.D．About 1.5 hours. 2．What’s the top speed of Fuxing train from Lhasa to Nyingchi?A．160 kilometers per hour.B．121 kilometers per hour.C．47 kilometers per hour.D．160 meters per hour.3．What does the underlined word “shortens” mean in Chinese?A．克扣B．下降C．缩短D．缺少4．What’s special about the Lhasa—Nyingchi Railway?A．It covers a large area.B．It can carry passengers.C．The train goes through lots of tunnels and bridges.D．It’s China’s first electrified railway on the “Roof of the World”.5．What can we learn from the passage?A．The railway trip is not relaxing or fast.B．The railway does not bring more tourists to the area.C．The train goes through 121 tunnels and 47 bridges.D．It’s a bit slower than some other Fuxing trains in our country.B（2022·江苏南京·中考真题）Sue and Jim were neighbours for five years but they were never interested in each other. Then one day, Sue saw Jim’s music collection. She noticed a rare punk rock CD that she also owned. At that moment, she realized that they both had the same interest in music and they started talking. Sue said, “I thought we had nothing in common (相同) until I saw his CD by the New York Dolls.” They are now married and living with each other.Some psychologists (心理学家) think that your taste in music is related to (有联系的) your personality. As part of a test at the University of Texas, Austin, USA, volunteers created a CD of their favourite songs. The volunteers then listened to each other’s CDs and made guesses about the CD creator’s personality—outgoing, adventurous, happy, and so on. These strangers correctly guessed much more about each others’ personalities through their CDs than through their clothesor taste in films. For example, Sue and Jim love punk music, which means they have outgoing personalities.The psychologists who did the test found Snoop Dogg (hip-hop) fans are likely to be energetic and talkative. People who like U2 (rock/pop music) are generally independent and adventurous.The psychologists also found that Louis Armstrong (jazz) fans tend to (倾向于) be serious and intelligent. While fans of classical music are also likely to enjoy jazz music and tend to be shy. The psychologists were surprised to find that rap and heavy metal fans were also shier and quieter than many other music lovers.C（2022·重庆璧山·一模）Pollution inside homes and other buildings kills more than 4 million people each year. Many people die as a result of breathing smoke or from cooking over wood powered or coal powered stoves.One way to reduce the number of deaths is through cooking equipment (设备) powered by the sun. Crosby Menzies, a solar power expert in a South African company, described his latest solar cooker called the “Sol 4”. “It is four square meters of mirrors, six to eight meters in length. It is quite a large cooker.”The “Sol 4” works by reflecting (反射) light from the sun off large mirrors. The mirrors direct (把……对准) the light at a cooking pan. Then the light heats the pan with solar energy. Within two minutes, the pan is hot enough to cook sausages and onions. In just four minutes, water can be boiled. That is as fast as cooking with natural gas or electricity.The cooker is also much “friendlier” than other models because people do not have to stand in the sun to use it. And people can prepare meals without having to collect firewood or spend money on coal. Such individuals would be less likely to have breathing illnesses from harmful smoke.At present, the cooker only works when the sun is shining. But engineers are working on a way to make it work without sunlight.Each cooker costs about $2,000, which is a large amount of money for most Africans. To solve that problem, Crosby Menzies has created a nongovernmental (非官方) organization called Solar Cookers for Africa. It plans to raise money through donations (捐款) to provide poor people with solar cooking equipment.16．The writer starts the passage by ________.A．comparing facts B．sharing experiences C．listing numbers D．giving reasons 7．The underlined word “individuals” most probably means “________”.A．cookers B．people C．mirrors D．experts 18．Which of the following about the “Sol 4” is TRUE ?A．It is six to eight square meters of mirrors.B．It can work in any weather condition now.C．It can also heat the pan by lighting firewood.D．It is more environment-friendly than other cookers.19．From the passage, we can infer that ________.A．the “Sol 4” will be designed in a smaller sizeB．more Africans can use the solar cookers in the futureC．the “Sol 4” is the first product of the South African companyD．the African government will provide poor people with solar cookerD（2022·重庆开州·二模）◆Facial-recognition(人脸识别) cameras are everywhere, even in your smartphone. Many people use this technology to unlock their phones, open doors, or make quick payments. But during the pandemic(疫情), can it still work when people put on masks?◆The answer is no. Scientists are trying to solve this problem. For example, Engineering & Technology(E＆T) magazine reports that Hanwang, a tech company in Beijing, has made a new system(系统). It can recognize 95 percent of people when they wear masks.◆What’s the secret? It’s all about your eyes.◆Marios Savides from Carnegie Mellon University, US, studies facial recognition. As we get older, he says, our faces change shape. But the area around our eyes doesn’t. It stays the same—even if we put on weight. As long as there are enough data(数据) from the area of our eyes, the cameras can recognize us.◆Tech 5 is another facial-recognition company. The Swiss company’s product scans(扫描) the shape of your face. It also scans your iris(虹膜), which is the colored part of your eyes. Tech 5 hopes to leave out(忽略) all of the face below the nose.◆This kind of software is more important than just unlocking your phone. “It can search for criminals(罪犯) and terrorists(恐怖分子) even when they wear masks,” said Huang-Lei from Hanwang. Hanwang’s technology is used by Chinese police in places like subway stations and airports. Similar technology is used by the United States Air Force.◆Huang pointed out one big weakness: it fails when people wear both a mask and sunglasses. “In this situation, all of the key facial information is lost,” Lei said to E&T.20．The writer leads to the topic(话题) by ________.A．giving an example B．telling stories C．listing numbers D．raising a problem 21．The new system can recognize 95 percent people by scanning ________.A．the face B．the eyes C．the nose D．the mouth22．We can infer that the new system ________.A．can recognize all people with masks in the futureB．can help the police to look for criminalsC．has brought plenty of money to Tech 5D．can recognize a person even when he loses some weight 23．The best structure of this passage is ________.A．B．C．D．专项39 阅读理解之说明文＂英语说明文＂，顾名思义，就是一种以＂说明、解释＂为主要表达方式的英语文体。
100多个常用英语转化词
100多个常用英语转化词以下是100多个常用的英语转化词：1. 在家工作– work from home2. 线上学习– online learning3. 远程会议– remote meeting4. 社交距离– social distancing5. 个人防护装备– personal protective equipment (PPE)6. 新冠病毒– COVID-197. 疫苗接种– vaccine administration8. 医疗保健工作者– healthcare worker9. 感染控制– infection control10. 新常态– new normal11. 数字化转型– digital transformation12. 微信支付– WeChat Pay13. 移动支付– mobile payment14. 在线购物– online shopping15. 无现金支付– cashless payment16. 虚拟现实– virtual reality (VR)17. 增强现实– augmented reality (AR)18. 人工智能– artificial intelligence (AI)19. 云计算– cloud computing20. 物联网– Internet of Things (IoT)21. 网络安全– cyber security22. 数据隐私– data privacy23. 社交媒体– social media24. 数字营销– digital marketing25. 网络营销– online marketing26. 电子商务– e-commerce27. 可持续发展– sustainable development28. 绿色能源– green energy29. 循环经济– circular economy30. 生态友好– eco-friendly31. 大数据– big data32. 云存储– cloud storage33. 人脸识别– facial recognition34. 身份验证– identity verification35. 数据分析– data analysis36. 人工智能助手– AI assistant37. 虚拟助手– virtual assistant38. 智能家居– smart home39. 智能城市– smart city40. 智能交通– smart transportation41. 电子健康记录– electronic health record (EHR)42. 电子钱包– e-wallet43. 远程办公– remote work44. 在线协作– online collaboration45. 云会议– cloud meeting46. 数字签名– digital signature47. 电子签名– electronic signature48. 数字化学习– digital learning49. 虚拟学习– virtual learning50. 宽带互联网– broadband internet51. 无线网络– wireless network52. 电子邮件– email53. 动态驱动器– flash drive54. 数字相机– digital camera55. 蓝牙耳机– Bluetooth headphones56. 触屏设备– touch screen device57. 可穿戴设备– wearable device58. 视频会议– video conference59. 在线支付– online payment60. 科技创新– technological innovation61. 数据保护– data protection62. 电子商务平台– e-commerce platform63. 电子书籍– e-book64. 在线娱乐– online entertainment65. 人脑计算– brain-computer interface (BCI)66. 无人驾驶– autonomous driving67. 3D打印– 3D printing68. 虚拟货币– virtual currency69. 科技公司– technology company70. 网红– internet celebrity71. 视频分享– video sharing72. 在线音乐– online music73. 实时定位– real-time location74. 网购– online shopping75. 电子游戏– video game76. 自助服务– self-service77. 远程医疗– telemedicine78. 电子票务– e-ticketing79. 无线充电– wireless charging80. 电动汽车– electric vehicle (EV)81. 生物识别– biometric identification82. 智能手表– smartwatch83. 无人机– drone84. 虚拟帮手– virtual assistant85. 数码产品– digital products86. 国际化– internationalization87. 数据中心– data center88. 电子证照– electronic license89. 智能监控– intelligent surveillance90. 电子支付– electronic payment91. 电子商务平台– e-commerce platform92. 电子合同– electronic contract93. 数字货币– digital currency94. 无线耳机– wireless earphones95. 高科技产业– high-tech industry96. 电子竞技– e-sports97. 移动应用– mobile application98. 虚拟私人网络– virtual private network (VPN)99. 电子货币– e-currency100. 虚拟课堂– virtual classroom希望对你有帮助！。
Python使用face_recognition人脸识别
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。
人脸识别的基本方法
人脸识别的基本方法
一、人脸识别的基本原理
人脸识别（facial recognition）技术是一种生物识别技术，它可以
作为一种安全认证技术，通过通过对比个人的脸部特征和记录的脸部特征
进行鉴别的方式来确认个人身份，将真实的人脸和图像中的人脸进行对比
来达到鉴别此人的目的。
人脸识别技术的工作原理大致分为三个步骤：
1.特征提取：提取人脸图像的特征，这些特征包括脸型特征、眼睛特征、嘴巴特征、鼻子特征等；
2.特征向量化：将这些特征信息转换成特征向量，以便进行后续比对；
3.比对验证：把已经录入的特征向量和新输入的特征向量进行比较，
验证是否为同一个人的脸部特征。
1.基于模板的匹配方法
这是最常用的人脸识别方法，也是最常用的 biometric 系统之一、
这种方法的核心在于，在认证的过程中，将人脸信息预先存储在数据库中，然后将用户输入的人脸信息和数据库中已存储的信息进行匹配，通过比较
匹配度来确定这是否是同一个人，从而判断用户的身份。
2.基于深度学习的识别方法
在这种方法中，人脸识别系统首先会提取人脸信息，然后利用深度学
习技术，将提取的信息分析出脸部的特征数据，最后将这些特征进行比较，从而判断是否为同一个人。
人脸识别FR
人脸识别FR人脸识别（Facial Recognition）首章：引言人类作为社交动物，面对面的交流在日常生活中扮演着重要的角色。
人脸识别（Facial Recognition）技术的发展为我们带来了便利和安全性的提升。
本文将就人脸识别技术的原理、应用以及相关议题进行探讨。
第二章：人脸识别的基本原理1. 图像采集：人脸识别的第一步是通过摄像设备采集人脸图像。
摄像设备可以是监控摄像头、手机相机或者专用的人脸采集设备。
2. 预处理：为了提高人脸识别的准确性，采集到的图像需要进行预处理。
这包括人脸检测、图像的归一化和去噪等步骤。
3. 特征提取：通过一系列的数学算法，从预处理后的图像中提取人脸的特征。
这些特征可能包括脸部的轮廓、眼睛、嘴巴等特征点的位置和形状。
4. 特征匹配：将提取到的人脸特征与已有的人脸数据库进行比对，寻找与之最接近的人脸。
匹配的方法可以是基于相似度比较的算法，如欧几里德距离或者余弦相似度等。
第三章：人脸识别的应用人脸识别技术的广泛应用使得我们的生活变得更加便利和安全。
以下是几个常见的应用领域：1. 解锁手机：现代智能手机广泛应用了人脸识别技术，使得用户可以通过简单的自拍动作快速解锁手机。
2. 支付验证：一些移动支付平台也开始采用人脸识别技术进行用户身份验证，提高支付的安全性。
3. 身份识别：在边境检查、机场安检、银行业务等环节中，人脸识别技术能够快速准确地识别身份，提高安全性并节省时间。
4. 犯罪抓捕：警方可以通过监控摄像头中的人脸识别技术，快速锁定嫌疑人，并进行追踪和抓捕。
第四章：人脸识别的挑战和争议虽然人脸识别技术在许多领域得到了广泛应用，但也存在一些挑战和争议。
主要包括：1. 隐私问题：人脸识别技术需要采集用户的个人生物信息，这引发了对个人隐私保护的担忧，并引发了隐私法律的争议。
2. 误识别问题：人脸识别技术在特定情况下可能会出现误识别现象，例如在光线不好或者穿戴口罩时。
这可能导致无辜人士受到错误的定罪。
酒店入住人脸识别登记流程
酒店入住人脸识别登记流程英文回答：Hotel Guest Registration with Facial Recognition.Facial recognition technology is becoming increasingly common in the hospitality industry, as hotels seek to improve security and efficiency. By using facial recognition, hotels can streamline the guest registration process, making it faster and more convenient for guests.Benefits of Facial Recognition for Hotel Guest Registration.There are a number of benefits to using facial recognition for hotel guest registration, including:Speed and efficiency: Facial recognition can significantly reduce the time it takes to register a guest. This is because guests do not need to provide theirpersonal information or fumble with a key card.Security: Facial recognition is a highly secure way to verify a guest's identity. This is because facialrecognition systems are very accurate and difficult to fool.Convenience: Facial recognition is a very convenient way for guests to register. This is because guests do not need to remember their room number or carry around a key card.How Facial Recognition Works for Hotel Guest Registration.Facial recognition systems work by comparing a guest's face to a database of known faces. When a guest arrives ata hotel, they will be asked to stand in front of a camera. The camera will take a picture of the guest's face and compare it to the database. If the guest's face matches a face in the database, they will be allowed to register.Implementation of Facial Recognition for Hotel GuestRegistration.Hotels can implement facial recognition for guest registration by following these steps:1. Purchase a facial recognition system: There are a number of different facial recognition systems available on the market. Hotels should research different systems tofind one that meets their needs.2. Install the facial recognition system: The facial recognition system should be installed in a secure location at the hotel.3. Train the facial recognition system: The facial recognition system must be trained with a database of known faces. Hotels can collect this database from their existing guests or from a third-party provider.4. Test the facial recognition system: The facial recognition system should be tested before it is put into use. This will ensure that the system is working properly.Conclusion.Facial recognition is a valuable tool that can help hotels improve security, efficiency, and convenience. By using facial recognition for guest registration, hotels can make the check-in process faster, more secure, and more convenient for guests.中文回答：酒店入住人脸识别登记流程。
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Questions
1988
1991
Real time automated face recognition
Implemented in 2001 super bowl
FERET
1993-1997 The Face Recognition Technology Evaluation Sponsored by Defense Advances Research Products Agency
Fishers Faces
Maximize between-class variance Minimize within-class variance
EBGM
Relies on concept of nonlinear features
Lighting Pose
Expression
Given a still or video image of a scene, Identify or verify one or more persons in the scene using a stored database of faces
A Brief History
1960’s
Facial Recognition
By Lisa Tomko
Overview
What is facial recognition? History The Face Recognition Technology Evaluation
The Face Recognition Vender Test
Facial Recognition Grand Challenge Principle Components Analysis Linear Description Analysis Elastic Bunch Graph Matching
Βιβλιοθήκη Applications
Research
What is facial recognition?
Images must all be the same size and normalized Uses Data compression to reduce the detentions of the data and removes information that is not useful Decomposes facial structure into orthogonal components known as eigenfaces, stored in a 1D array
First semi-automated system Programs designed by Woody Bledsoe, Helen Chan Wolf and Charles Bisson. Linear algebra technique implemented Less than 100 values needed to align and normalize a face. Turk and Pentland
Entertainment
Areas of Research
MIT-multidimensional morphable models, view-based human face detection, cortex-like mechanisms, and object detection by components.
High Resolution photos 3D Face Scans Iris images
10x more effective then 2002 100x more effective than 1995
PCA
Pioneered by Kirby and Sirivich in 1988
Creates a dynamic link architecture that projects the face onto a grid Garbor jet is a node which describes image behaviors around pixels Garbor filter extracts shapes and detects features Accurate land mark localization is needed
Encouraged development of face recognition algorithms
FRVT
2000, 2002, and 2006
The Face Recognition Vender Test Evaluate work of FERET Assess commercial facial recognition Educate public
Pro: Only needs 1/1000 of data presented
Con: Needs full frontal face
LDA
Each face is represented by a large number of pixel values
Used to reduce number of features to a more manageable number before classification
In 2002- 90% verification and 1% false accept rates
FRGC
Facial Recognition Grand Challenge
Evaluated the latest in face recognition algorithms Used:
MSU-data clustering, statistical pattern recognition, face detection in color images, the use of faces and fingerprints for personal identification, and kernel principal component analysis. UCSD- use of shape contexts for object recognition, slow feature analysis, classifying facial actions, and face recognition using independent component analysis.
Applications
Law enforcement
Facial recognition using various databases SocialCamera SceneTap FaceR Celebrity TV set top box
Mobile applications