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==Method==
==Method==
DeepFace begins by using aligned versions of several existing databases to improve the algorithms and produce a normalized output.<ref>{{Cite journal|last=Ramachandra|first=Raghavendra|last2=Busch|first2=Christoph|date=2017-03-20|title=Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey|url=https://doi.org/10.1145/3038924|journal=ACM Computing Surveys|volume=50|issue=1|pages=8:1–8:37|doi=10.1145/3038924|issn=0360-0300}}</ref><ref>{{Cite journal|last=Arachchilage|first=Samadhi|date=2020|title=Deep-learned faces: a survey|url=https://www.researchgate.net/publication/342541739_Deep-learned_faces_a_survey/link/5fc4689ea6fdcc6cc6840349/download|journal=EURASIP Journal on Image and Video Processing}}</ref> However, these models are insufficient to produce effective facial recognition in all instances. DeepFace uses fiducial point detectors based on existing databases to direct the alignment of faces. The facial alignment begins with a 2D alignment, and then continues with 3D alignment and frontalization. That is, DeepFace’s process is two steps. First, it corrects the angles  of an image so that the face in the photo  is looking forward. To accomplish this, it uses a 3-D model  of  a face. Then the deep learning produces a numerical description of the face. If DeepFace comes up with a similar enough description for two images, it assumes that these two images share a face.
DeepFace begins by using aligned versions of several existing databases to improve the algorithms and produce a normalized output.<ref>{{Cite journal|last=Ramachandra|first=Raghavendra|last2=Busch|first2=Christoph|date=2017-03-20|title=Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey|url=https://doi.org/10.1145/3038924|journal=ACM Computing Surveys|volume=50|issue=1|pages=8:1–8:37|doi=10.1145/3038924|issn=0360-0300}}</ref><ref>{{Cite journal|last=Arachchilage|first=Samadhi|date=2020|title=Deep-learned faces: a survey|url=https://www.researchgate.net/publication/342541739_Deep-learned_faces_a_survey/link/5fc4689ea6fdcc6cc6840349/download|journal=EURASIP Journal on Image and Video Processing}}</ref> However, these models are insufficient to produce effective facial recognition in all instances. DeepFace uses fiducial point detectors based on existing databases to direct the alignment of faces. The facial alignment begins with a 2D alignment, and then continues with 3D alignment and frontalization. That is, DeepFace’s process is two steps. First, it corrects the angles  of an image so that the face in the photo  is looking forward. To accomplish this, it uses a 3-D model  of  a face. Then the deep learning produces a numerical description of the face. If DeepFace comes up with a similar enough description for two images, it assumes that these two images share a face<ref>{{Cite web|title=DeepFace: Closing the Gap to Human-Level Performance in Face Verification|url=https://research.fb.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/|access-date=2021-04-25|website=Facebook Research|language=en-US}}</ref>.


'''2D Alignment'''
'''2D Alignment'''

Revision as of 20:22, 25 April 2021

DeepFace is a deep learning facial recognition system created by a research group at Facebook. It identifies human faces in digital images. It employs a nine-layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users.[1][2] DeepFace shows human-level performance. The Facebook Research team has stated that the DeepFace method reaches an accuracy of 97.35% ± 0.25% on Labeled Faces in the Wild (LFW) data set where human beings have 97.53%.[3] This means that DeepFace is sometimes more successful than the human beings. However, DeepFace model falls behind Google FaceNet which got 99.65% on the same data set.[4] It also leaves behind the FBI's Next Generation Identification system which have 85% performance [5] One of the creators of the software, Yaniv Taigman, came to Facebook via their 2007 acquisition of Face.com.[6]

Commercial rollout

Origin

DeepFace was produced by a collection of Facebook’s artificial intelligence scientists including Yainiv Taigman and Facebook research scientist Ming Yang. They were also joined by Lior Wolf, a faculty member from Tel Aviv University. Yaniv Taigman, came to Facebook when Facebook acquired Face.com in 2007.

Facebook started rolling out the technology to its users in early 2015,[7] continuously expanding DeepFace's use and software. DeepFace, according to the director of Facebook’s artificial intelligence research, is not intended to invade individual privacy. Instead, DeepFace alerts individuals when their face appears in any photo posted on Facebook. When they receive this  notification, they have the  option of removing their face from the photo.[8]

European Union

When the technology was initially deployed, users had the option to turn DeepFace off but were not notified that it was on.[9] DeepFace was not released in the European Union due to data privacy laws there. Local technology regulators in Europe argued that Facebook’s facial recognition did not comply with EU data protection laws because users do not consent to all uses of their biometric data.[10]

Efficacy in Comparison

DeepFace systems can identify faces with 97% accuracy, almost the same accuracy as a human in the same position. Facebook’s facial recognition is more effective than the FBI’s technology, which has 85% accuracy.[11] Google’s technology, FaceNet is more successful than DeepFace using the same data sets. FaceNet set a record for accuracy, 99.63%. Google’s FaceNet incorporates data from Google Photos.[12]

Current Uses

Following the release of DeepFace in 2015, its uses have remained fairly stagnant. Because more individuals have uploaded images to Facebook, the algorithm has gotten more accurate and been capable of identifying more and more faces. Facebook’s DeepFace is the largest facial recognition dataset that currently exists. Some individuals argue that  as Facebook’s facial ID database expands it could potentially be distributed to government agencies and be used in ways that individuals do not allow[13] In response to privacy concerns, Facebook removed their automatic facial recognition feature – allowing individuals to opt in to tagging through DeepFace. This change was implemented in 2019.

Facebook uses individual facial recognition templates to find photos that an individual is in so that they can review, engage, or share the content. They also claim that they use facial recognition to help protect individuals from impersonation or identity misuse. Take, for example, an instance where an individual used someone’s profile photo  as their own.  Through DeepFace,  Facebook can identify and alert the person whose  information is being misused.[14] To ensure that individuals have control over their facial recognition, Facebook does not share facial templates. Additionally, Facebook will remove images from facial recognition templates if someone has deleted their account or untagged themself from a photo. Individuals also have the ability to turn their facial recognition off on facebook. If they say that they do not want Facebook to be able to recognize them in photos and videos, Facebook will cease facial recognition for that individual.  

Method

DeepFace begins by using aligned versions of several existing databases to improve the algorithms and produce a normalized output.[15][16] However, these models are insufficient to produce effective facial recognition in all instances. DeepFace uses fiducial point detectors based on existing databases to direct the alignment of faces. The facial alignment begins with a 2D alignment, and then continues with 3D alignment and frontalization. That is, DeepFace’s process is two steps. First, it corrects the angles  of an image so that the face in the photo  is looking forward. To accomplish this, it uses a 3-D model  of  a face. Then the deep learning produces a numerical description of the face. If DeepFace comes up with a similar enough description for two images, it assumes that these two images share a face[17].

2D Alignment

The DeepFace process begins by detecting 6 fiducial points on the detected face — the center of the eyes, tip of the nose and mouth location. These points are translated onto a warped image to help detect the face. However, 2D transformation fails to compensate for rotations that are out of place.

3D Alignment

In order to align faces, DeepFace uses a generic 3D model wherein 2D images are cropped as 3D versions. The 3D image has 67 fiducial points. After the image has been warped, there are 67 anchor points manually placed on the image to match the 67 fiducial points. A 3D-to-2D camera is then fitted that minimizes losses. Because 3D detected points on the contour of the face can be inaccurate, this step is important.

Frontalization

Because full perspective projections are not modeled, the fitted camera is only an approximation of the individual’s actual face. To reduce errors, DeepFace aims to warp the 2D images with smaller distortions. Also, thee camera P is capable of replacing parts of the image and blending them with their symmetrical counterparts.

Reactions

Industry Reaction

AI researcher Ben Goertzel said Facebook had "pretty convincingly solved face recognition" with the project, but said it would be incorrect to conclude that deep learning is the entire solution to AI.

Neeraj Kumar, a researcher at the University of Washington said that Facebook’s DeepFace shows how large sets of outside data can result in a “higher capacity” model. Because of Facebook’s wide access to images of individuals, their facial recognition software can perform comparatively better than other softwares with much smaller data sets.[18][19]

Media Reaction

A Huffington Post piece called the technology "creepy" and, citing data privacy concerns, noting that some European governments had already required Facebook to delete facial-recognition data.[20] According to Broadcasting & Cable, both Facebook and Google had been invited by the Center for Digital Democracy to attend a 2014 National Telecommunications and Information Administration "stakeholder meeting" to help develop a consumer privacy Bill of Rights, but they both declined. Broadcasting & Cable also noted that Facebook had not released any press announcements concerning DeepFace, although their research paper had been published earlier in the month. Slate said the lack of publicity from Facebook was "probably because it's wary of another round of headlines decrying its creepiness.

User Reaction

Many individuals fear facial recognition technology.[21][22] The technology’s nearly perfect accuracy allows social media companies and the government to create digital ideas of millions of Americans.[23] However, an individual’s fear of facial recognition and other privacy concerns does not correspond to a decrease in social media use. Instead, attitudes towards privacy and privacy settings do not have a large impact on an individual’s intention to use Facebook apps.[24][25][26] Because Facebook is a social media site, individual fears about privacy get over ruled by a desire to participate in social media.[27]

Privacy Concerns

BIPA Lawsuit

Facebook users raised a class action lawsuit against Facebook under Illinois Biometric Information Privacy Act (BIPA).[28] Illinois has the most comprehensive biometric privacy legislation, regulating the collection of biometric information by commercial entities.[29] Illinois’ BIPA requires a corporation that obtains a person’s biometric information to obtain a written release, provide them notice that their information is being collected, and state the duration the information will be collected. The lawsuit alleges that Facebook’s collection of facial identification information for the purpose of the tag suggestion tool violates BIPA.[30] Because Facebook does not give notice or consent to individuals when they use this tool, Facebook users argue that it violates BIPA.[31]  The Ninth Circuit denied Facebook’s motion to dismiss the case and ultimately certified the case. Facebook sought to appeal to the certification of the Ninth Circuit decision which was ultimately granted. Facebook claims that the case should not have been verified because Plaintiffs have no alleged any harm beyond Facebook’s violation of BIPA. Facebook removed their automatic facial recognition tagging feature in 2019, in response to the concerns raised in the lawsuit.[32] Facebook proposed a $550 million settlement to the case, which was rejected. When Facebook increased the settlement to $650 million, the court accepted it. Facebook was ordered to pay their $650 million  settlement in early March 2021. 1.6 million residents of Illinois will receive at least $345 dollars.[33]

Racism in Facial Identification Technology

Facial recognition algorithms are not universally successful.[34] While the algorithms are capable of classifying faces with over 90% accuracy in some cases, accuracy is lower when the algorithms are pled to women, black individuals, and young people.[35] The systems falsely identify black and asian faces 10 to 100 times more than they do with white faces.[36] Because algorithms are primarily trained with white men, systems like DeepFace have a more difficult time identifying them.[37] Scientists believe that once facial recognition data bases are trained to identify people of color — exposing them to more diverse faces — they will be more successful at identification.[38]

In July 2020, Facebook announced that it is building teams that will look into racism in its algorithms.[39] Facebook’s teams will work with Facebook’s Responsible AI team to study bias in their systems. The implementation of these programs is recent, and it is still unclear what reforms will be made.[40]

10 Year Challenge

In 2019, a Facebook challenge went viral asking users to post a photo from 10 years ago and one from 2019. The challenge was coined the “10 Year challenge.”  More than 5 million people participated in the challenge, including many celebrities. Worry arose that Facebook’s 10 year challenge was designed to train Facebook’s facial recognition database. Kate O’Neill, a writer for Wired, wrote an op-ed that echoed this possibility.[41] Facebook denied that they played a role in generating the challenge.[42] Instead, they argued that it was a user generated challenge that allowed individuals to have fun online. However, individuals have argued that the concerns that underscore theories around the 10 year challenge are echoed by broader concerns about Facebook and the right to privacy.[43]     

See also

References

  1. ^ "Facebook creates software that matches faces almost as well as you do", Technology Review, Massachusetts Institute of Technology, March 17, 2014
  2. ^ "Facebook's DeepFace shows serious facial recognition skills", CBS News, March 19, 2014
  3. ^ "DeepFace: Closing the Gap to Human-Level Performance in Face Verification". Facebook Research. Retrieved 2019-07-25.
  4. ^ "LightFace: A Lightweight Deep Face Recognition Framework". IEEE. doi:10.1109/ASYU50717.2020.9259802. S2CID 227220279. Retrieved 2020-12-13.
  5. ^ Russell Brandom (July 7, 2014), "Why Facebook is beating the FBI at facial recognition", The Verge
  6. ^ Amit Chowdhry (March 18, 2014), "Facebook's DeepFace Software Can Match Faces With 97.25% Accuracy", Forbes
  7. ^ Chowdhry, Amit. "Facebook's DeepFace Software Can Match Faces With 97.25% Accuracy". Forbes. Retrieved 2021-04-09.
  8. ^ Chowdhry, Amit. "Facebook's DeepFace Software Can Match Faces With 97.25% Accuracy". Forbes. Retrieved 2021-04-08.
  9. ^ "Facebook settles facial recognition dispute". BBC News. 2020-01-30. Retrieved 2021-04-08.
  10. ^ "Vol 23.1 – Winter 2017 | Journal of Science & Technology Law". www.bu.edu. Retrieved 2021-04-24.
  11. ^ "Facial Recognition Technology: Ensuring Transparency in Government Use — FBI". www.fbi.gov. Retrieved 2021-04-09.
  12. ^ "Facial recognition: top 7 trends (tech, vendors, markets, use cases & latest news)". Thales Group. Retrieved 2021-04-09.
  13. ^ Glaser, April (2019-07-09). "Facebook's Face-ID Database Could Be the Biggest in the World. Yes, It Should Worry Us". Slate Magazine. Retrieved 2021-04-22.
  14. ^ "What is the face recognition setting on Facebook and how does it work? | Facebook Help Center". www.facebook.com. Retrieved 2021-04-22.
  15. ^ Ramachandra, Raghavendra; Busch, Christoph (2017-03-20). "Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey". ACM Computing Surveys. 50 (1): 8:1–8:37. doi:10.1145/3038924. ISSN 0360-0300.
  16. ^ Arachchilage, Samadhi (2020). "Deep-learned faces: a survey". EURASIP Journal on Image and Video Processing.
  17. ^ "DeepFace: Closing the Gap to Human-Level Performance in Face Verification". Facebook Research. Retrieved 2021-04-25.
  18. ^ "Facebook Creates Software That Matches Faces Almost as Well as You Do". MIT Technology Review. Retrieved 2021-04-22.
  19. ^ Rubinstein, Ira; Good, Nathan (2012). "Privacy by Design: A Counterfactual Analysis of Google and Facebook Privacy Incidents". SSRN Electronic Journal. doi:10.2139/ssrn.2128146. ISSN 1556-5068.
  20. ^ Gr, Dino; oni (2014-03-18). "Facebook's New 'DeepFace' Program Is Just As Creepy As It Sounds". HuffPost. Retrieved 2021-04-22.
  21. ^ "Privacy and identity on Facebook", Discourse and Identity on Facebook, Bloomsbury Academic, ISBN 978-1-4742-8912-2, retrieved 2021-04-24
  22. ^ Barrett, Lindsey (2020-07-24). "Ban Facial Recognition Technologies for Children—And for Everyone Else". Rochester, NY. {{cite journal}}: Cite journal requires |journal= (help)
  23. ^ Huang, Michelle Yan. "Facial recognition is almost perfectly accurate — here's why that could be a problem". Business Insider. Retrieved 2021-04-22.
  24. ^ "Information privacy behavior in the use of Facebook apps: A personality-based vulnerability assessment". Heliyon. 6 (8): e04714. 2020-08-01. doi:10.1016/j.heliyon.2020.e04714. ISSN 2405-8440.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  25. ^ Mathiyalakan, Sathasivam; Heilman, George; Ho, Kevin; Law, Wai (2018-01-01). "An Examination of the Impact of Gender and Culture on Facebook Privacy and Trust in Guam". Journal of International Technology and Information Management. 27 (1): 29–56. ISSN 1941-6679.
  26. ^ "Facebook face recognition hits privacy protests". Biometric Technology Today. 2011 (7): 1. July 2011. doi:10.1016/s0969-4765(11)70120-5. ISSN 0969-4765.
  27. ^ Rosenthal, Sonny; Wasenden, Ole-Christian; Gronnevet, Gorm-Andreas; Ling, Rich (2020-11-01). "A tripartite model of trust in Facebook: acceptance of information personalization, privacy concern, and privacy literacy". Media Psychology. 23 (6): 840–864. doi:10.1080/15213269.2019.1648218. ISSN 1521-3269.
  28. ^ "Power, Pervasiveness and Potential: The Brave New World of Facial Recognition Through a Criminal Law Lens (and Beyond)". nycbar.org. Retrieved 2021-03-31.
  29. ^ "The rise and regulation of thermal facial recognition technology during the COVID-19 pandemic" - Google Search". www.google.com. Retrieved 2021-04-22.
  30. ^ Center, Electronic Privacy Information. "EPIC - Patel v. Facebook". epic.org. Retrieved 2021-04-22. {{cite web}}: |first= has generic name (help)
  31. ^ "Social Network or Social Nightmare: How California Courts Can Prevent Facebook's Frightening Foray Into Facial Recognition Technology From Haunting Consumer Privacy Rights Forever". vLex. Retrieved 2021-04-24.
  32. ^ "An Update About Face Recognition on Facebook". About Facebook. 2019-09-03. Retrieved 2021-04-22.
  33. ^ "Facebook will pay $650 million to settle class action suit centered on Illinois privacy law". TechCrunch. Retrieved 2021-04-22.
  34. ^ Becerra-Riera, Fabiola; Morales-González, Annette; Méndez-Vázquez, Heydi (2019-08-01). "A survey on facial soft biometrics for video surveillance and forensic applications". Artificial Intelligence Review. 52 (2): 1155–1187. doi:10.1007/s10462-019-09689-5. ISSN 0269-2821.
  35. ^ Buolamwini, Joy; Gebru, Timnit (2018-01-21). "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification". Conference on Fairness, Accountability and Transparency. PMLR: 77–91.
  36. ^ "govinfo". www.govinfo.gov. Retrieved 2021-04-23.
  37. ^ "Bias in, Bias out: Why Legislation Placing Requirements on the Procurement of Commercialized Facial Recognition Technology Must Be Passed to Protect People of Color". www.americanbar.org. Retrieved 2021-04-23.
  38. ^ Kane, Kane; Young, Amber; Majchrzak, Ann; Ransbotham, Sam (2021-03-01). "Avoiding an Oppressive Future of Machine Learning: A Design Theory for Emancipatory Assistants". Management Information Systems Quarterly. 45 (1): 371–396. ISSN 0276-7783.
  39. ^ Heilweil, Rebecca (2020-07-22). "Facebook is taking a hard look at racial bias in its algorithms". Vox. Retrieved 2021-04-23.
  40. ^ Trautman, Lawrence J. (2020-03-27). "Governance of the Facebook Privacy Crisis". Pittsburgh Journal of Technology Law & Policy. 20 (1). doi:10.5195/tlp.2020.234. ISSN 2164-800X.
  41. ^ "Facebook's '10 Year Challenge' Is Just a Harmless Meme—Right?". Wired. ISSN 1059-1028. Retrieved 2021-04-22.
  42. ^ "https://twitter.com/facebook/status/1085675097766031360". Twitter. Retrieved 2021-04-22. {{cite web}}: External link in |title= (help)
  43. ^ Slobom, Michael (2020-01-01). "Consent, Appropriation by Manipulation, and the 10-Year Challenge: How an Internet Meme Complicated Biometric Information Privacy". Mitchell Hamline Law Review. 46 (5).

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Further reading

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