Facial recognition in one article

How facial recognition works

Facial recognition is the process of identifying or verifying the identity of a person using their face. It captures, analyzes, and compares patterns based on the person's facial details.

1. The face detection process is an essential step as it detects and locates human faces in images and videos.
2.  The face capture process transforms analog information (a face) into a set of digital information (data) based on the person's facial features.
3. The face match process verifies if two faces belong to the same person.

面部识别是使用面部识别或验证一个人身份的过程。 它根据人的面部细节捕获，分析和比较模式。

1. 面部检测过程是必不可少的步骤，因为它可以检测并定位图像和视频中的人脸。
2.   面部捕捉过程基于人的面部特征将模拟信息（面部）转换为一组数字信息（数据）。
3. 人脸匹配过程验证两个人脸是否属于同一个人。

Face recognition data to identify and verify

Biometrics are used to identify and authenticate a person using a set of recognizable and verifiable data unique and specific to that person.

For more on Face recognition SDK, visit our saas web dossier.

Identification answers the question: "Who are you?"

Authentication answers the question: "Are you really who you say you are?"

Stay with us. Here are some examples : 

In the case of facial biometrics, a 2D or 3D sensor "captures" a face. It then transforms it into digital data by applying an algorithm before comparing the image captured to those held in a database.

These automated systems can be used to identify or check individuals' identity in just a few seconds based on their facial features (geometry): spacing of the eyes, bridge of the nose, the contour of the lips, ears, chin, etc.

They can even do this in the middle of a crowd and within dynamic and unstable environments. Proof of this can be seen in the performance achieved by Thales' Live Face Identification System (LFIS), an advanced solution resulting from our long-standing expertise in biometrics.  

Owners of the iPhone X have already been introduced to facial recognition technology. 

Of course, other signatures via the human body also exist, such as fingerprints, iris scans, voice recognition, digitization of veins in the palm, and behavioral measurements. 

人脸识别数据以识别和验证

生物识别技术用于使用针对该人的唯一且特定的一组可识别且可验证的数据来识别和认证该人。

有关面部识别SDK的更多信息，请访问我们的SAAS网络。

身份证明回答了这个问题：“你是谁？”

身份验证可以回答以下问题：“您真的说的是谁吗？”

让我们看一下，这里有些例子 ：

在进行面部生物识别的情况下，2D或3D传感器可以“捕获”面部。然后，在将捕获的图像与数据库中保存的图像进行比较之前，通过应用算法将其转换为数字数据。

这些自动化系统可用于根据个人面部特征（几何形状）在短短几秒钟内识别或检查个人身份：眼睛间距，鼻梁，嘴唇，耳朵，下巴的轮廓等。

他们甚至可以在人群中间以及动态和不稳定的环境中执行此操作。泰雷兹的实时面部识别系统（LFIS）所取得的性能可以证明这一点，这是一种先进的解决方案，这是由于我们在生物识别技术方面的长期专业知识而产生的。

iPhone X的所有者已经被引入了面部识别技术。

当然，还存在其他通过人体的签名，例如指纹，虹膜扫描，语音识别，手掌静脉数字化以及行为测量。

Why face recognition, then? 

Facial biometrics continues to be the preferred biometric benchmark. 

That's because it's easy to deploy and implement. There is no physical interaction required by the end-user. 

Moreover, face detection and face match processes for verification/identification are speedy.

那么为什么要面部识别呢？

面部生物识别仍然是首选的生物识别基准。

这是因为它易于部署和实施。 最终用户不需要进行任何物理交互。

而且，用于验证/识别的面部检测和面部匹配处理是快速的。

#1 Top facial recognition technologies

In the race for biometric innovation, several projects are vying for the top spot.

Google, Apple, Facebook, Amazon, and Microsoft (GAFAM) are also very much in the mix. 

All the software web giants now regularly publish their theoretical discoveries in artificial intelligence, image recognition, and face analysis to further our understanding as rapidly as possible.

Let’s take a closer look :

Academia

The GaussianFace algorithm developed in 2014 by researchers at The Chinese University of Hong Kong achieved facial identification scores of 98.52% compared with the 97.53% achieved by humans. An excellent rating, despite weaknesses regarding memory capacity required and calculation times.

Facebook and Google

In 2014, Facebook announced its DeepFace program, which can determine whether two photographed faces belong to the same person, with an accuracy rate of 97.25%. When taking the same test, humans answer correctly in 97.53% of cases, or just 0.28% better than the Facebook program. 

In June 2015, Google went one better with FaceNet. On the widely used Labeled Faces in the Wild (LFW) dataset, FaceNet achieved a new record accuracy of 99.63%  (0.9963 ± 0.0009).   

Using an artificial neural network and a new algorithm, the company from Mountain View has managed to link a face to its owner with almost perfect results.  

This technology is incorporated into Google Photos and used to sort pictures and automatically tag them based on the people recognized. Proving its importance in the biometrics landscape, it was quickly followed by the online release of an unofficial open-source version known as OpenFace. 

Microsoft, IBM, and Megvii

A study done by MIT researchers in February 2018 found that Microsoft, IBM, and China-based Megvii (FACE++) tools had high error rates when identifying darker-skin women compared to lighter-skin men.

At the end of June 2018, Microsoft announced in a blog post that it had substantially improved its biased facial recognition technology.   

Amazon

In May 2018, Ars Technica reported that Amazon is already actively promoting its cloud-based face recognition service named Rekognition to law enforcement agencies. The solution could recognize as many as 100 people in a single image and can perform face matches against databases containing tens of millions of faces. 

In July 2018, Newsweek reported that Amazon’s facial recognition technology falsely identified 28 US Congress members as people arrested for crimes. 

Key biometric matching technology providers

At the end of May 2018, the US Homeland Security Science and Technology Directorate published the results of sponsored tests at the Maryland Test Facility (MdTF). These real-life tests measured the performance of 12 face recognition systems in a corridor measuring 2 m by 2.5 m. 

Thales' solution utilizing a Facial recognition software (LFIS) achieved excellent results with a face acquisition rate of 99.44% in less than 5 seconds (against an average of 68%), a Vendor True Identification Rate of 98% in less than 5 seconds compared with an average 66%. It also achieved an error rate of 1% compared with an average of 32%. 

＃1顶级面部识别技术

在生物识别技术创新竞赛中，有几个项目正在争夺头把交椅。

谷歌，苹果，Facebook，亚马逊和微软（GAFAM）也在其中。

现在，所有软件网络巨头都定期发布其在人工智能，图像识别和面部分析方面的理论发现，以尽快加深我们的理解。

让我们仔细看看：

香港中文大学的研究人员于2014年开发的高斯脸部算法获得了98.52％的面部识别分数，而人类则为97.53％。尽管在所需的存储容量和计算时间方面存在弱点，但仍为出色的评价。

Facebook和谷歌

2014年，Facebook宣布了DeepFace程序，该程序可以确定两张被拍照的面孔是否属于同一个人，准确率达到97.25％。当进行相同的测试时，人类可以在97.53％的情况下正确回答，或者仅比Facebook程序好0.28％。

2015年6月，Google在FaceNet方面做得更好。在广泛使用的“野生标签的脸”（LFW）数据集中，FaceNet的新记录准确度达到了99.63％（0.9963±0.0009）。

通过使用人工神经网络和新算法，Mountain View的公司成功地将脸部与其所有者联系在一起，从而获得了近乎完美的效果。

这项技术已整合到Google相册中，用于对图片进行分类并根据识别出的人自动为其添加标签。为了证明其在生物识别领域的重要性，紧随其后的是在线发布了称为OpenFace的非官方开源版本。

Microsoft，IBM和Megvii

麻省理工学院研究人员于2018年2月进行的一项研究发现，与肤色较浅的男性相比，微软，IBM和中国的Megvii（FACE ++）工具在识别肤色较黑的女性时具有较高的错误率。

2018年6月底，微软在博客文章中宣布，它已经大大改善了偏见的面部识别技术。

亚马逊

2018年5月，Ars Technica报告称亚马逊已经在积极向执法机构推广其基于云的面部识别服务Rekognition。该解决方案可以在一幅图像中识别多达100个人，并且可以对包含数千万张面孔的数据库进行面孔匹配。

2018年7月，《新闻周刊》报道说，亚马逊的面部识别技术错误地将28名美国国会议员识别为犯罪嫌疑人。

关键的生物识别匹配技术提供商

2018年5月底，美国国土安全科学与技术局在马里兰州测试设施（MdTF）上发布了赞助测试的结果。这些实际测试测试了在2 m x 2.5 m的走廊中12个面部识别系统的性能。

Thales使用面部识别软件（LFIS）的解决方案获得了出色的结果，在不到5秒的时间内面部识别率达到了99.44％（平均为68 ％），与之相比，不到5秒的供应商真实识别率达到了98％平均为66％。与平均32％的错误率相比，它也实现了1％的错误率。

#2 Learning to learn through deep learning

The feature common to all these disruptive technologies is Artificial Intelligence (AI) and, more precisely, deep learning, where a system can learn from data.

Why is it important?

Face recognition systems are getting better all the time. 

According to a recent NIST report, massive gains in recognition accuracy have been made in the last five years (2013- 2018) and exceed improvements achieved in the 2010-2013 period. 

Most of the face recognition algorithms in 2018 outperform the most accurate algorithm from late 2013.

In its 2018 test, NIST found that 0.2% of searches in a database of 26.6 million photos failed to match the correct image, compared with a 4% failure rate in 2014. 

In NIST's 2020 tests, the best facial identification algorithm has an error rate of 0.08% - that's less than one error for 1.000 images. (source: How accurate are facial recognition systems, CSIS)

＃2通过深度学习来学习

所有这些破坏性技术的共同特征是人工智能（AI），更准确地说是深度学习，系统可以在其中从数据中学习。

它为什么如此重要？

人脸识别系统一直在不断完善。

根据NIST最近的一份报告，在过去的五年（2013-2018年）中，识别准确性取得了巨大的进步，超过了2010-2013年期间所取得的进步。

2018年的大多数人脸识别算法优于2013年末以来最准确的算法。

NIST在2018年的测试中发现，在2660万张照片的数据库中，有0.2％的搜索未能匹配正确的图像，而2014年则为4％。

在NIST的2020年测试中，最佳面部识别算法的错误率为0.08％-小于1.000张图像的错误率。 （资料来源：面部识别系统CSIS的准确性如何）

#3 Facial recognition markets

Face recognition markets

A study published in June 2019 estimates that by 2024, the global facial recognition market would generate $7billion of revenue, supported by a compound annual growth rate (CAGR) of 16% over the period 2019-2024.

For 2019, the market was estimated at $3.2 billion.

The two most significant drivers of this growth are surveillance in the public sector and numerous other applications in diverse market segments.

According to the study, the top facial recognition vendors include :

Accenture, Aware, BioID, Certibio, Fujitsu, Fulcrum Biometrics, Thales, HYPR, Idemia, Leidos, M2SYS, NEC, Nuance, Phonexia, and Smilepass. Understand the face recognition SDK.

The main facial recognition applications can be grouped into three principal categories.

＃3面部识别市场

人脸识别市场

2019年6月发布的一项研究估计，到2024年，全球面部识别市场将产生70亿美元的收入，在2019-2024年期间的复合年增长率（CAGR）为16％。

对于2019年，市场估计为32亿美元。

增长的两个最重要的驱动因素是公共部门的监控以及不同市场领域的许多其他应用。

根据这项研究，顶级的面部识别厂商包括：

埃森哲，意识，BioID，Certibio，富士通，Fulcrum Biometrics，Thales，HYPR，Idemia，Leidos，M2SYS，NEC，Nuance，Phonexia和Smilepass。了解人脸识别SDK应用。

主要的面部识别应用程序可以分为三个主要类别。

What is facial recognition used for?  

Here are the top three application categories where facial recognition is being used.

面部识别有什么用？

以下是使用面部识别的前三个应用类别。

1. Security - law enforcement

Automated fingerprint identification system

Forensic specialists can use Automated Biometric Identification Systems (ABIS) to compare multiple types of biometrics.

This market is led by increased activity to combat crime and terrorism.

The benefits of facial recognition systems for policing are evident: detection and prevention of crime.

Facial recognition is used when issuing identity documents and, most often, combined with other biometric technologies such as fingerprints (preventing ID fraud and identity theft). 

Face match is used at border checks to compare the portrait on a digitized biometric passport with the holder's face.

 Face biometrics can also be employed in police checks, although its use is rigorously controlled in Europe. In 2016, the "man in the hat" responsible for the Brussels terror attacks was identified thanks to FBI facial recognition software. The South Wales Police implemented it at the UEFA Champions League Final in 2017.

In the United States, 26 states (and probably as many as 30) allow law enforcement to run searches against their databases of driver’s license and ID photos. The FBI has access to driver’s license photos of 18 states.

Drones combined with aerial cameras offer an interesting combination for facial recognition applied to large areas during mass events. According to the Keesing Journal of Documents and Identity of June 2018, some hovering drone systems can carry a 10-kilo camera lens that can identify a suspect from 800 meters to a height of 100 meters. As the drone can be connected to the ground via a power cable, it has an unlimited power supply. The communication to ground control can’t be intercepted as it also uses a cable.

Facial recognition CCTV systems can improve performance in carrying public security missions. Let's illustrate this with four examples:

 Find missing children and disoriented adults.

 Identify and find exploited children.

 Identify and track criminals.

•  Support and accelerate investigationsFind missing children and disoriented adults.
•  Identify and find exploited children.
•  Identify and track criminals.
•  Support and accelerate investigations.

1.安全-执法

自动指纹识别系统

法医专家可以使用自动生物特征识别系统（ABIS）来比较多种类型的生物特征。

该市场以打击犯罪和恐怖主义的活动增多为主导。

面部识别系统在维持治安方面的好处显而易见：侦查和预防犯罪。

在签发身份证明文件时使用面部识别，并且通常与面部识别等其他生物识别技术结合使用（防止ID欺诈和身份盗用）。

脸部比对用于边界检查，以将数字化生物特征护照上的肖像与持有人的脸进行比较。

 脸部生物识别技术也可以用于警察检查，尽管在欧洲严格控制其使用。 2016年，借助FBI面部识别软件，确定了负责布鲁塞尔恐怖袭击的“戴帽子的人”。南威尔士州警察局在2017年欧洲冠军联赛决赛中将其实施。

在美国，有26个州（可能多达30个州）允许执法部门对其驾驶执照和身份证照片数据库进行搜索。 FBI可以访问18个州的驾驶执照照片。

无人机与航拍相机相结合，提供了一种有趣的组合，可用于大规模事件期间应用于大面积的面部识别。根据2018年6月的Keesing Documents and Identity杂志，一些悬停的无人机系统可以携带10公斤的摄像头，可以识别从800米到100米高的犯罪嫌疑人。由于无人机可以通过电源线连接到地面，因此它具有无限的电源。地面控制通讯也使用电缆，因此无法被截获。

面部识别CCTV系统可以提高执行公共安全任务的性能。让我们用四个示例说明这一点：

•  寻找失踪的孩子和迷失方向的成年人。
•  找出并找到被剥削的孩子。
•  识别并跟踪罪犯。
•  支持并加速调查。

2. Health

Significant advances have been made in this area. 

Thanks to deep learning and face analysis, it is already possible to:

• track a patient's use of medication more accurately，
• detect genetic diseases such as DiGeorge syndrome with a success rate of 96.6%，
• support pain management procedures.

2.健康

在这方面已经取得了重大进展。

借助深度学习和面部分析，已经可以：

• 更准确地跟踪患者的用药情况；
• 检出DiGeorge综合征等遗传疾病，成功率为96.6％；
• 支持疼痛管理程序。

3. Banking and retail

This area is undoubtedly the one where the use of facial recognition was least expected. And yet, quite possibly, it promises the most.

Know Your Customer (KYC) with facial recognition online is sure to be a hot topic in 2021.

Why? 

Because 64% of primary checking account openings were done online in Q2 2020 ( and 36% in branch) in the United States alone.

The pandemic has accelerated this emerging dynamic, and many branches are temporarily closed.

Besides, increased mobile usage urges businesses to have a mobile-first focus and develop fully mobile user-friendly onboarding experiences. 

During the selfie process, to avoid fraud using a static image, a liveness detection shall be provided by the technology. 

Liveness detection proves that the selfie taken comes from a live person.

The result?

Adapting to current customer preferences, financial intitutions (FIs) invest in digital onboarding through online and mobile channels.

3.银行和零售

毫无疑问，这一领域是最不希望使用面部识别的领域。 然而，很有可能它是最大的应用场景。

在线认识人脸识别（KYC）肯定会成为2021年的热门话题。

为什么？

因为仅在美国，64％的主要支票账户开户在2020年第二季度就完成了（分支机构的开立率为36％）。

大流行加速了这种新出现的趋势，许多分支机构暂时关闭。

此外，越来越多的移动设备使用率促使企业首先关注移动设备，并开发完全移动用户友好的入门体验。

在自拍照过程中，为了避免使用静态图像进行欺诈，该技术应提供活跃性检测。

活跃度检测证明所拍摄的自拍照来自真人。

结果？

为了适应当前客户的偏好，金融机构（FI）通过在线和移动渠道投资于数字化入门。

#4 Mapping of new users

While the United States currently offers the largest market for face recognition opportunities, the Asia-Pacific region sees the fastest growth in the sector. China and India lead the field. 

Face recognition in China

Face recognition technology is the new hot topic in China, from banks and airports to police. 

Now authorities are expanding the facial recognition sunglasses program as police are beginning to use them in Beijing's outskirts. 

China is also setting up and perfecting a video surveillance network countrywide.

＃4新用户地图

尽管美国目前为人脸识别提供了最大的市场，但亚太地区却是该领域增长最快的市场。 中国和印度处于领先地位。

中国人脸识别

从银行，机场到警察，面部识别技术是中国新的热门话题。

现在，随着警察开始在北京郊区使用面部识别太阳镜计划，当局正在扩大面部识别太阳镜计划。

中国还在全国范围内建立和完善视频监控网络。

According to CNBC, over 200 million surveillance cameras were in use at the end of 2018, and over 500 million are expected by 2021.

The facial recognition towers in Chinese cities are emblematic of this move.

This is linked to the social credit system the Chinese government is developing. 

In the TOP 10 cities with the most street cameras per person, Chongqing, Shenzhen, Shanghai, Tianjin, and Ji’nan are leading the pack.

London is #6 and Atlanta #10, according to the Guardian of 2 December 2019.

Chinese police are working with artificial intelligence companies such as Yitu, Megvii (in partnership with Huawei), SenseTime, and CloudWalk, according to The New York Times of 14 April 2019. 

China's ambitions in AI (and facial recognition technology) are high. The country aims to become a world leader in AI by 2030. 

Surprisingly, China provides strong biometric data protection against private entities AND increases the government's access to personal information.

根据CNBC的数据，截至2018年底，使用了超过2亿个监控摄像头，预计到2021年将超过5亿个。

中国城市中的面部识别塔就是这一举动的象征。

这与中国政府正在发展的社会信用体系有关。

在人均路灯摄像机数量最多的10个城市中，重庆，深圳，上海，天津和济南名列前茅。

根据2019年12月2日的《卫报》报道，伦敦排名第六，亚特兰大排名第十。

根据2019年4月14日的《纽约时报》，中国警方正在与人工智能公司如Yitu，Megvii（与华为合作），SenseTime和CloudWalk合作。

中国在人工智能（和面部识别技术）领域的抱负很高。该国的目标是到2030年成为AI的世界领导者。

令人惊讶的是，中国为私人实体提供了强大的生物识别数据保护，并增加了政府对个人信息的访问。

Facial recognition in Asia

Facial recognition will be a significant topic for the 2020 Olympic Games in Tokyo (postponed to September 2021). This technology will be used to identify authorized persons and grant them access automatically, enhancing their experience and safety. It's also being used in Japan for easier mobile banking access.

In Sydney, face recognition is undergoing trials at airports to help move people through security much faster and safer.

In India, the Aadhaar project is the largest biometric database in the world. It already provides a unique digital identity number to 1.27 billion residents as of December 2020.

UIDAI, the authority in charge, announced that facial authentication would be launched in a phased roll-out by September 2018. Face authentication will be available as an add-on service in fusion mode along with one more authentication factor like a fingerprint, Iris, or OTP. 

India could also roll-out the world's most extensive face recognition system in 2021. 

The National Crime Records Bureau (NCRB) has issued an RFP inviting bids to develop a nationwide facial recognition system. According to the 160-page document, the system will be a centralized web application hosted at the NCRB Data Center in Delhi. It will be available for access to all the police stations.  

It will automatically identify people from CCTV videos and images. The Bureau states that it will help police catch criminals, find missing people, and identify dead bodies.

亚洲人脸识别

面部识别将成为东京2020年奥运会（推迟到2021年9月）的重要主题。该技术将用于识别授权人员并自动授予他们访问权限，从而提高其经验和安全性。日本也正在使用它来简化移动银行的访问。

在悉尼，人脸识别正在机场进行试验，以帮助人们更快，更安全地通过安全检查。

在印度，Aadhaar项目是世界上最大的生物识别数据库。截至2020年12月，它已经为12.7亿居民提供了唯一的数字身份号码。

主管机构UIDAI宣布，面部认证将在2018年9月之前分阶段推出。面部认证将作为融合服务的一项附加服务，以及诸如指纹，虹膜，或OTP。

印度也可能在2021年推出世界上最广泛的人脸识别系统。

国家犯罪记录局（NCRB）已发布RFP邀请，以开发全国性的面部识别系统。根据160页的文件，该系统将是一个位于德里的NCRB数据中心的集中式Web应用程序。它将可用于所有警察局。

它将自动从CCTV视频和图像中识别人员。该局表示，它将帮助警察抓获罪犯，寻找失踪人员并识别尸体。

Other large projects

In Brazil, the Superior Electoral Court (Tribunal Superior Eleitoral) is involved in a nationwide biometric data collection project. The aim is to create a biometric database and unique ID cards by 2020, recording the information of 140 million citizens. 

Russia's Central Bank has been deploying a countrywide program since 2017 designed to collect faces, voices, iris scans, and fingerprints. But the process is progressing very slowly, according to the Biometricupdate website of 13 March 2019.

Moscow claims one of the world’s largest network of 160,000 surveillance cameras by the end of 2019 and is to be fitted with facial recognition technology for public safety.

The roll-out started in January 2020.

Russian law does not regulate non-consensual face detection and analysis.

其他大型项目

在巴西，最高选举法院（Triplenal Superior Eleitoral）参与了一项全国性的生物识别数据收集项目。目标是到2020年创建一个生物识别数据库和唯一的身份证，记录1.4亿公民的信息。

俄罗斯中央银行自2017年以来一直在部署一项全国计划，旨在收集面部，声音，虹膜扫描和指纹。但是根据2019年3月13日的Biometricupdate网站，该过程进展非常缓慢。

莫斯科宣称，到2019年底，该网络将成为全球最大的160,000个监控摄像头网络之一，并将配备面部识别技术以保障公共安全。

首次部署于2020年1月开始。

俄罗斯法律不规范未经同意的人脸检测和分析。

#5 When face recognition strengthens the legal system

The ethical and societal challenge posed by data protection is radically affected by the use of facial recognition technologies. 

Do these technological feats, worthy of science-fiction novels, genuinely threaten our freedom? 

And with it, our anonymity? 

EU and UK biometric data protection

In Europe and the UK, the General Data Protection Regulation (GDPR) provides a rigorous framework for these practices.

Any investigations into a citizen's private life or business travel habits are out of the question, and any such invasions of privacy carry severe penalties. 

Applicable from May 2018, the GDPR supports the principle of a harmonized European framework, in particular protecting the right to be forgotten and the giving of consent through clear affirmative action.

Yes, you read it well. There's now one law for 500 million people.

This directive is bound to have international repercussions. 

US biometric data protection landscape

In America, the State of Washington was the third US state (after Illinois and Texas) to formally protect biometric data through a new law introduced in June 2017. 

California was the fourth state as of January 2020.

The California Consumer Privacy Act (CCPA) passed in June 2018 and effective as of 1 January 2020, will have a serious impact on privacy rights and consumer protection not only for residents of California but for the whole nation.

The law is frequently presented as a model for a federal data privacy law. 

In that sense, the CCPA has the potential to become as consequential as the GDPR.

In July 2018, Bradford L. Smith, Microsoft’s president, compared the face recognition technology to products like highly regulated medicines, and he urged Congress to study it and oversee its use. 

In May 2019, US Rep. Alexandria Ocasio-Cortez voiced her "absolute" concerns in a recent Committee Hearing on facial recognition Technology (Impact on our Civil Rights and Liberties).

More recently, a New York State law called the Stop Hacks and Improve Electronic Data Security (SHIELD) became effective 21 March 2020. It requires the implementation of a cybersecurity program and protective measures for NY State residents. 

The act applies to businesses that collect the personal information of NY residents.

With the act, New York now stands beside California.

Facial recognition bans (San Francisco, Somerville, Oakland, San Diego, Boston, Portland)

Privacy and civil rights concerns have escalated in the country as face recognition gains traction as a law enforcement tool, and, on 6 May 2019, San Francisco voted to ban facial recognition.

It is the first ban of its kind on the use of face recognition.

The anti-surveillance ordinance signed by San Francisco's Board of Supervisors bars city agencies, including San Francisco PD, from using the technology as of June 2019. 

Yes, this includes law enforcement.

As reported by the Boston Globe on 27 June 2019, the Somerville City Council (Massachusetts) voted to ban facial recognition, making the city the second community to make such a decision.

Lather, rinse, repeat.

• On 16 July 2019, Oakland (California) took the same decision and became the third US city to ban the use of face recognition technology. It is interesting to note that the Oakland Police department is not using this technology and was not planning to use it.
• San Diego took the same decision at the end of December 2019 in advance of the new Californian law. This new law (Assembly Bill 215) about facial recognition and other biometric surveillance) specifically prohibits the use of police body cameras in California. The ban is in place for three years as of 1 January 2020.
• On 24 June 2020, Boston voted to ban face surveillance technology by police, as reported by the Boston Herald.
• Portland (Oregon) decided its ban on 9 September 2020. The city is the first city to extend it to "private entities in places of public accommodation" such as private stores. (CNN).

Since the San Francisco, Sommerville, Oakland, and now San Diego Boston, and Portland rulings, the debate gets louder in many cities and not only in the U.S.

In Europe, at the end of August 2019, Sweden's Data Protection Authority decided to ban facial recognition technology in schools and fined a local high school (the first GDPR penalty in the country).

How to better regulate emerging technologies?

So,

• Should other cities or countries follow this example?
• Is the ban just a "pause button" to better assess risks?
• Is this a step backward for public safety?
• Is there a policy vacuum? At which level?

Stay tuned for the outcome of all these discussions as the US Congress is getting pressure from activists to ban the technology and from providers) to regulate.

But there's still no Federal legal framework to address the issue as of January 2021.

The EU Commission is planning to act on the indiscriminate use of facial identifier technology. The European Commission president Ursula von der Leyen wants a coordinated approach to the human and ethical implications of artificial intelligence. She has pledged to publish an AI legislation blueprint very soon.

The final version of the European commission whitepaper is available online.

Again the questions of privacy, consent, and function creep (data collected for one purpose being used for another)are central to the debate.

Find more on biometric data protection laws(EU, UK, and US perspective) in our biometric data dossier.

India and its national biometric identification scheme, Aadhaar

In India, thanks to the Puttaswamy judgment delivered on 27 August 2017, the Supreme Court has enshrined the right to privacy in the country's constitution. This decision has rebalanced the relationship between citizen and state and posed a new challenge to the expansion of the Aadhaar project.

The Indian government, however, approved the use of the country's biometric EID program by private entities on 28 February 2019.

Rebound effect: the legal system and its professions get even stronger. 

As both ambassadors and guardians of data protection regulation, data protection officers have become necessary for businesses and a much sought-after role. 

＃5当人脸识别法制化时

面部识别技术的使用从根本上影响了数据保护所带来的道德和社会挑战。

这些值得做科幻小说的技术壮举，是否真的在威胁着我们的自由？

有了它，我们的匿名性？

欧盟和英国生物特征数据保护

在欧洲和英国，通用数据保护条例（GDPR）为这些做法提供了严格的框架。

不可能对公民的私人生活或商务旅行习惯进行任何调查，任何这种侵犯隐私的行为都会受到严厉的惩罚。

GDPR自2018年5月起适用，支持统一的欧洲框架原则，特别是通过明确的平权行动来保护被遗忘的权利和给予同意。

是的，您读得很好。现在有一部针对5亿人口的法律。

该指令注定会产生国际影响。

美国生物识别数据保护格局

在美国，华盛顿州是美国的第三个州（仅次于伊利诺伊州和德克萨斯州），并于2017年6月推出了一项新法律，正式保护生物识别数据。

截至2020年1月，加利福尼亚州是第四州。

于2018年6月通过并于2020年1月1日生效的《加利福尼亚消费者隐私法案》（CCPA）将不仅对加利福尼亚居民而且对整个国家都严重影响隐私权和消费者保护。

该法律经常作为联邦数据隐私法的典范提出。

从这个意义上讲，CCPA有可能变得与GDPR一样重要。

2018年7月，微软总裁布拉德福德·史密斯（Bradford L. Smith）将面部识别技术与高度管制的药物等产品进行了比较，他敦促国会对其进行研究并监督其使用。

2019年5月，美国众议员亚历山大·奥卡西奥·科特斯（Alexandria Ocasio-Cortez）在最近的一次面部识别技术委员会听证会（对我们的公民权利和自由的影响）中表达了她的“绝对”关注。

最近，一项名为“制止黑客和改善电子数据安全（SHIELD）”的纽约州法律于2020年3月21日生效。该法律要求实施网络安全计划和对纽约州居民的保护措施。

该法案适用于收集纽约居民个人信息的企业。

通过该法案，纽约现在站在加利福尼亚旁边。

禁止面部识别（旧金山，萨默维尔，奥克兰，圣地亚哥，波士顿，波特兰）

随着人脸识别作为一种执法工具而受到关注，该国的隐私和公民权利问题日益升级.2019年5月6日，旧金山投票通过了禁止人脸识别的法案。

这是使用面部识别技术的第一个禁令。

截至2019年6月，旧金山市监督委员会签署的反监督条例禁止包括旧金山PD在内的城市机构使用该技术。

是的，这包括执法。

据《波士顿环球报》 2019年6月27日报道，萨默维尔市议会（马萨诸塞州）投票决定禁止面部识别，这使该市成为第二个做出此类决定的社区。

泡沫，冲洗，重复。

2019年7月16日，奥克兰（加利福尼亚州）做出了同样的决定，成为美国第三个禁止使用面部识别技术的城市。有趣的是，奥克兰警察局并未使用该技术，也未计划使用该技术。

在新的加利福尼亚法律之前，圣地亚哥于2019年12月底做出了相同的决定。这项有关面部识别和其他生物识别监视的新法律（第215号大会法案）明确禁止在加利福尼亚使用警用人体摄像机。该禁令自2020年1月1日起实施，为期三年。

根据《波士顿先驱报》的报道，2020年6月24日，波士顿投票决定禁止警察使用脸部监视技术。

波特兰（俄勒冈州）于2020年9月9日宣布了禁令。该市是第一个将其扩展到“公共住宿场所的私人实体”（例如私人商店）的城市。 （CNN）。

自从旧金山，索默维尔，奥克兰，以及现在的圣地亚哥波士顿以及波特兰的裁决以来，在许多城市，不仅在美国，辩论越来越激烈。

在欧洲，2019年8月下旬，瑞典数据保护局决定禁止在学校使用面部识别技术，并对当地一所高中处以罚款（该国首次对GDPR进行处罚）。

如何更好地规范新兴技术？

所以，

• 其他城市或国家应该效法吗？
• 禁令仅仅是为了更好地评估风险的“暂停按钮”吗？
• 这是为了公共安全倒退吗？
• 有政策真空吗？在哪个级别？

请密切注意所有这些讨论的结果，因为美国国会正受到激进主义者施加压力，要求禁止该技术以及要求提供者进行监管。

但是到2021年1月，仍然没有解决该问题的联邦法律框架。

欧盟委员会正计划对面部识别器技术的不加区分的使用采取行动。

欧盟委员会主席乌尔苏拉·冯·德·莱恩（Ursula von der Leyen）希望采用协调一致的方法来应对人工智能对人类和伦理的影响。她已承诺很快会发布AI立法蓝图。

欧盟委员会白皮书的最终版本可在线获得。

隐私，同意和功能蠕变（出于一种目的而收集的数据被用于另一种目的）的问题再次成为辩论的中心。

在我们的生物特征数据档案中查找有关生物特征数据保护法律的更多信息（欧盟，英国和美国的观点）。

印度及其国家生物识别计划Aadhaar

在印度，由于2017年8月27日的Puttaswamy判决，最高法院将隐私权纳入了该国宪法。这项决定重新平衡了公民与国家之间的关系，并对Aadhaar项目的扩展提出了新的挑战。

但是，印度政府于2019年2月28日批准了私人实体使用该国的生物识别EID计划。

反弹效应：法律制度及其专业变得更加强大。

作为数据保护法规的代言人和监护人，数据保护人员已成为企业所必需的角色，并广受追捧。

#6 The rebels – facial recognition hackers

Despite this technical and legal arsenal designed to protect data, citizens, and their anonymity, critical voices have still been raised. 

Some parties are concerned and alarmed by these developments. Some have taken action. 

But can facial recognition be fooled?

• In Russia, Grigory Bakunov has invented a solution to escape the eyes permanently watching our movements and confuse face detection devices. He has developed an algorithm that creates special makeup to fool the software. However, he has chosen not to bring his product to market after realizing how easily criminals could use it.
• In Germany, Berlin artist Adam Harvey has come up with a similar device known as CV Dazzle. He is now working on clothing featuring patterns to prevent detection. The hyperface camouflage includes patterns in fabric, such as eyes and mouths, to fool the face recognition system.
• In late 2017, a Vietnamese company successfully used a mask to hack the Face ID face recognition function of Apple's iPhone X. However, the hack is too complicated to implement for large-scale exploitation.
• Around the same time, researchers from a German company revealed a hack that allowed them to bypass the facial authentication of Windows 10 Hello by printing a facial image in infrared.
• Forbes announced in an article from May 2018 that researchers from the University of Toronto have developed an algorithm to disrupt facial recognition software (aka privacy filter).
• In August 2020, the Verge detailed a "cloaking" app named Fawkes. The software imperceptibly distorts your selfies and other pics you may leave on social media. The tool is coming from the University of Chicago’s Sand Lab.

＃6反叛者-面部识别黑客

尽管设计了这种技术和法律武器库来保护数据，公民及其匿名性，但仍提出了批评的声音。

一些方面对这些事态发展感到关切和震惊。一些已经采取了行动。

但是人脸识别可以被攻克吗？

• 在俄罗斯，格里高里·巴库诺夫（Grigory Bakunov）发明了一种解决方案，可以永久地注视着我们的动作来使眼睛逃避并混淆面部检测设备。他开发了一种算法，该算法可以创建特殊的外观来欺骗软件。但是，在意识到犯罪分子可以轻松使用产品之后，他选择不将其产品推向市场。
• 在德国，柏林艺术家亚当·哈维（Adam Harvey）提出了一种类似的设备，称为CV Dazzle。他现在正在研究带有图案的服装，以防止被发现。超脸伪装包括织物上的图案，例如眼睛和嘴巴，以使人脸识别系统蒙上阴影。
• 2017年底，一家越南公司成功使用面具来破解Apple iPhone X的Face ID人脸识别功能。但是，这种破解太复杂了，无法大规模实施。
• 大约在同一时间，一家德国公司的研究人员发现了一个黑客，该黑客通过使用红外线打印面部图像来绕过Windows 10 Hello的面部身份验证。
• 福布斯在2018年5月发布的一篇文章中宣布，多伦多大学的研究人员已经开发出一种破坏面部识别软件（又名隐私过滤器）的算法。
• 2020年8月，Verge推出了一款名为Fawkes的“隐形”应用程序。该软件会无形中扭曲您可能留在社交媒体上的自拍照和其他照片。该工具来自芝加哥大学的沙子实验室。

#7 Further together – towards hybridized solutions

The identification and authentication solutions of the future will borrow from all aspects of biometrics. 

This will lead to a biometric mix capable of guaranteeing total security and privacy for all stakeholders in the ecosystem. 

In this kind solution, geolocation, IP-addresses (the device being used), and keying patterns can create a strong combination to authenticate users for on-line banking or egovernment services securely.

This seventh trend belongs to us all together.

It's our job to envisage it together and make it happen through high-added-value biometric projects.

＃7携手并进–迈向混合解决方案

未来的识别和认证解决方案将借鉴生物识别技术的各个方面。

这将导致能够确保生态系统中所有利益相关者的总体安全性和隐私性的生物特征混合。

在人脸识别解决方案中，地理位置，IP地址（正在使用的设备）和密钥模式可以创建强大的组合，以安全地验证用户的在线银行服务或政府服务。

这第七个趋势属于我们所有参与者，我们共同设想并通过高附加值生物识别项目实现它是我们的工作。

Face recognition and you.

Now it's your turn.

Indeed, we can't claim to predict all the essential topics that will emerge in the short term future. 

Can you fill in some of the gaps?

If you've something to say on facial recognition, tech, or trends, a question to ask, or have simply found this article useful, please leave a comment in the box below. 

If you have a VIVO phone, you can quickly test the SDK.

LiveFaceDemo.apk

I look forward to hearing from you.

ONEClOUD：

• This article introduces face recognition from 7 latitudes, which are:
• # Top facial recognition technologies
• #Learning to learn through deep learning
• #Facial recognition markets
• #Mapping of new users
• #When face recognition strengthens the legal system
• #The rebels – facial recognition hackers
• #Further together – towards hybridized solutions
• #An example of face recognition SDK application