Augmented Reality
Augmented Reality
With focus on facial recognition and social networking
cdpatel1 [1]
evmarbella [2]
Thesis Statement
Thesis
To what extent has HCI in facial recognition improved in a social network driven society.
Keywords
- Augmented Reality
- Mobile image search
- Mobile devices and productivity
- Face Recognition
- Social Networks
- Camera
- Tagging
- Ubiquity
- Multimodal Biometrics
- Cameraphone photos and sharing
Bibliography
http://library.senecacollege.ca/Find_Articles/
http://library.senecacollege.ca/resources/ebsco_asp.html
Primary
Choi, Kwontaeg, Kar-Ann Toh, and Hyeran Byun. "Realtime training on mobile devices for face recognition applications." Pattern Recognition 44.2 (2011): 386-400. Academic Search Premier. EBSCO. Web. 27 Oct. 2010. http://bit.ly/ceVBRT
ROKITA, JOANNA, ADAM KRZYŻAK, and CHING Y. SUEN. "MULTIMODAL BIOMETRICS BY FACE AND HAND IMAGES TAKEN BY A CELL PHONE CAMERA." International Journal of Pattern Recognition & Artificial Intelligence 22.3 (2008): 411-429. Academic Search Premier. EBSCO. Web. 27 Oct. 2010. http://bit.ly/dzSsbz
Van House, N., Davis, M., Ames, M., Finn, M., and Viswanathan, V. 2005. The uses of personal networked digital imaging: an empirical study of cameraphone photos and sharing. In CHI '05 Extended Abstracts on Human Factors in Computing Systems (Portland, OR, USA, April 02 - 07, 2005). CHI '05. ACM, New York, NY, 1853-1856. DOI= http://doi.acm.org/10.1145/1056808.1057039
Mavridis, N., Kazmi, W., Toulis, P., and Ben-Abdelkader, C. 2009. On the Synergies between Online Social Networking, Face Recognition and Interactive Robotics. In Proceedings of the 2009 international Conference on Computational Aspects of Social Networks (June 24 - 27, 2009). CASON. IEEE Computer Society, Washington, DC, 48-56. DOI= http://dx.doi.org/10.1109/CASoN.2009.28 http://www.irml.uaeu.ac.ae/irml/images/Publications/mavkaz_cason2009.pdf
NOV, ODED, and YE CHEN. "Why Do People Tag? Motivations for Photo Tagging." Communications of the ACM 53.7 (2010): 128-131. Academic Search Premier. EBSCO. Web. 1 Nov. 2010. http://web.ebscohost.com/ehost/detail?vid=1&hid=108&sid=abe7b0c2-e47b-4a82-82aa-d35152353d38%40sessionmgr111&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#db=aph&AN=8558868
Secondary
Menglei Jia, Xin Fan, Xing Xie, Mingjing Li, and Wei-Ying Ma. 2006. Photo-to- Search: Using Camera Phones to Inquire of the Surrounding World. In Proceedings of the 7th International Conference on Mobile Data Management (MDM '06). IEEE Computer Society, Washington, DC, USA, 46-. DOI=10.1109/MDM.2006.122 http://dx.doi.org/10.1109/MDM.2006.122
Reilly, R. B. 1998. Applications of face and gesture recognition for human-computer interaction. In Proceedings of the Sixth ACM international Conference on Multimedia: Face/Gesture Recognition and their Applications (Bristol, United Kingdom). MULTIMEDIA '98. ACM, New York, NY, 20-27. DOI= http://doi.acm.org/10.1145/306668.306677
Kong, S. G., Heo, J., Abidi, B. R., Paik, J., and Abidi, M. A. 2005. Recent advances in visual and infrared face recognition: a review. Comput. Vis. Image Underst. 97, 1 (Jan. 2005), 103-135. DOI= http://dx.doi.org/10.1016/j.cviu.2004.04.001
Ames, M. and Naaman, M. 2007. Why we tag: motivations for annotation in mobile and online media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (San Jose, California, USA, April 28 - May 03, 2007). CHI '07. ACM, New York, NY, 971-980. DOI= http://doi.acm.org/10.1145/1240624.1240772
Choi, J. Y., De Neve, W., Ro, Y. M., and Plataniotis, K. N. 2009. Face annotation for personal photos using collaborative face recognition in online social networks. In Proceedings of the 16th international Conference on Digital Signal Processing (Santorini, Greece, July 05 - 07, 2009). IEEE Press, Piscataway, NJ, 240-247.
Tertiary
Pranav Mistry - Sixth Sense Project http://www.wired.com/epicenter/2009/02/ted-digital-six/f http://www.pranavmistry.com/projects/sixthsense/
John Maeda - The Laws of Simplicity http://lawsofsimplicity.com/ (Web)
Face detection and gesture recognition for human-computer interaction By Ming-Hsuan Yang, Narendra Ahuja (google books)[ http://books.google.com/books?id=zS0AZgdAJBsC&lpg=PP1&ots=iwY1ShiMHM&dq=first%20hand%20research%20in%20face%20recognition&pg=PP1#v=onepage&q=first%20hand%20research%20in%20face%20recognition&f=false]
Face Detection and Gesture Recognition for Human-Computer Interaction (ACM)[ http://portal.acm.org/citation.cfm?id=558181&coll=Portal&dl=GUIDE&CFID=109120439&CFTOKEN=76327335]
A camera-based mobile data channel: capacity and analysis (ACM)[ http://portal.acm.org/citation.cfm?id=1220973.1221344&coll=Portal&dl=GUIDE&CFID=109120439&CFTOKEN=76327335]
Wikipedia - Face Detection (Wikipedia)[ http://en.wikipedia.org/wiki/Face_detection]
Research Notes
Introduction & Definition of Face Recognition Technology
http://en.wikipedia.org/wiki/Facial_recognition_system#Early_development
How does the technology work (overview)?
Effectiveness of Point Distribution Model and Active Shape Models:
“The point distribution model can be used for Face detection and face recognition.”
ROKITA, JOANNA, ADAM KRZYŻAK, and CHING Y. SUEN. "MULTIMODAL BIOMETRICS BY FACE AND HAND IMAGES TAKEN BY A CELL PHONE CAMERA." International Journal of Pattern Recognition & Artificial Intelligence 22.3 (2008): 411-429. Academic Search Premier. EBSCO. Web. 1 Nov. 2010.
Uses of the technology.
Facial recognition for personal security:
“ face detection methods can be categorized into the following two large groups:
1) heuristic-based methods and 2) classification-based methods.”
“The following two popular ways of doing the face registration can be found in the literature: 1) holistic
methods and 2) local methods.”
“Illumination normalization, therefore, is a very important preprocessing step before verification.”
Face Verification is math heavy This calculates thresholds (I think). We will worry about that later. Even Chris would love to explain this heavy math, if we need his help.
Information Fusion
“From each frame, we obtain a value of its likelihood ratio and compare it to a threshold to make the decision”
Facial recognition for fighting terrorism:
“A technique such as face recognition could, at least in principle, be used to recognize people
“passively,” without their knowledge or cooperation.”
“Instead of a person continuously monitoring the video, the goal is to have a computer monitor the
video and alert a human operator if an “interesting person” is in view.”
System Dependencies: “the system can only recognize the persons whose images have been enrolled in the gallery”
“If a terrorist is known by name and reputation, but no picture of the terrorist is available, then the face
recognition system is useless.”
“system must be able to acquire face images of reasonable quality to use as probes.”
“system has a sensitivity threshold that must be set appropriately.”
Bowyer, Kevin W. "Face Recognition Technology: Security versus Privacy." IEEE Technology & Society Magazine 23.1 (2004): 9-20. Academic Search Premier. EBSCO. Web. 1 Nov. 2010.
Facial recognition for search realistically.
“Imagine never forgetting a face, and always being able to recognize a friend-of-a-friend on the street. And because the systems arelinked to the Internet, once AR recognized someone, you could easily pull up his or her online footprint, such as a Facebook page.”
Cascio, Jamais. "Seeing Too Much." Atlantic Monthly (10727825) 304.4 (2009): 34. Academic Search Premier. EBSCO. Web. 1 Nov. 2010.
Problems due to technology.
Facial recognition vs. privacy in social media driven world:
1. “The United States Constitution declares a level of protection for the rights of individual citizens
against oppression by their government that has made the U.S. unique. The right to privacy is one right that has become a firmly entrenched American value, even though it is not explicitly enumerated in the Constitution. Since face recognition systems represent leading-edge and rapidly changing technology, this technology is currently a major issue in the area of social impact of technology.”
Bowyer, Kevin W. "Face Recognition Technology: Security versus Privacy." IEEE Technology & Society Magazine 23.1 (2004): 9-20. Academic Search Premier. EBSCO. Web. 1 Nov. 2010.
2. 2. “We don’t regard ourselves as ‘Big Brother’. We’re more like a friendly uncle or aunt
watching over you.”
Hale, Benjamin. "Identity crisis: Face recognition technology and freedom of the will." Ethics, Place & Environment 8.2 (2005): 141-158. Academic Search Premier. EBSCO. Web. 1 Nov. 2010.
Facial recognition false positives.
“Distinctiveness of a face predicts both miss errors (MEs) and false positives (FPs) but correlations
between these errors are low”
Lewis, Michael B. "Familiarity, Target Set and False Positives in Face Recognition." European Journal of Cognitive Psychology 9.4 (1997): 437-459. Academic Search Premier. EBSCO. Web. 1 Nov. 2010.
Recorded Details
1. Introduction & Definition of Face Recognition Technology
http://en.wikipedia.org/wiki/Facial_recognition_system#Early_development
2. How does the technology work (overview)?
Effectiveness of Point Distribution Model and Active Shape Models: “The point distribution model can be used for Face detection and face recognition.”
ROKITA, JOANNA, ADAM KRZYŻAK, and CHING Y. SUEN. "MULTIMODAL BIOMETRICS BY FACE AND HAND IMAGES TAKEN BY A CELL PHONE CAMERA." International Journal of Pattern Recognition & Artificial Intelligence 22.3 (2008): 411-429. Academic Search Premier. EBSCO. Web. 1 Nov. 2010.
Recorded Details:
To find local facial features, such as eyes, mouth and nose, we can apply a point distribution model and active shape models. Point distribution model is used to generate facial points. Each shape in the face is represented by a set of n labeled landmark points, which must be consistent from one shape to the next. As an example, the first point may correspond to the right corner of the right eye. Given a set of such labeled training examples, we align them into a common coordinate frame. This is accomplished by translating, rotating and scaling each training shape so as to minimize the sum of squared distances to the mean of the set. Each shape can then be represented by a 2n-dimensional vector. This points are followed by some healthy mathematical calculations (will be explained in the draft).
3. Uses of the technology:
3.1 Facial recognition for Authentication and Authorization:
“Face detection methods can be categorized into the following two large groups: 1) heuristic-based methods and 2) classification-based methods.”
“The following two popular ways of doing the face registration can be found in the literature: 1) holistic methods and 2) local methods.”
“Illumination normalization, therefore, is a very important pre-processing step before verification.”
Information Fusion: “From each frame, we obtain a value of its likelihood ratio and compare it to a threshold to make the decision”
Recorded Details:
Personal authentication requires a different approach to the face detection and face recognition. For face detection, we can use either heuristic-based methods or classification-based methods. For face registration we can use either holistic methods or local methods. All the methods are followed by
heavy math (may be included in the draft). However, all these method can used for authorization or authentication. Instead of remembering the username and password, one can use face detection and recognition to authenticate the person to computer login, application login , and websites login. The idea of using face recognition for authentication also supports global movement of one username and password for all the online websites (global username and password).
3.2 Facial recognition for fighting terrorism:
“A technique such as face recognition could, at least in principle, be used to recognize people “passively,” without their knowledge or cooperation.”
“Instead of a person continuously monitoring the video, the goal is to have a computer monitor the video and alert a human operator if an “interesting person” is in view.”
System Dependencies:
“the system can only recognize the persons whose images have been enrolled in the gallery”
“If a terrorist is known by name and reputation, but no picture of the terrorist is available, then the face recognition system is useless.”
“system must be able to acquire face images of reasonable quality to use as probes.”
“system has a sensitivity threshold that must be set appropriately.”
Bowyer, Kevin W. "Face Recognition Technology: Security versus Privacy." IEEE Technology & Society Magazine 23.1 (2004): 9-20. Academic Search Premier. EBSCO. Web. 1 Nov. 2010.
Recorded Detail:
Recognizing a terrorist using a FRT might be useful having important assets monitored by cameras creates a system that is always looking for persons of interest. However, there are ways for the terrorists to avoid such a system due to it‟s dependencies. FRT can only recognize persons whose images have been included in a gallery, terrorist could possibly have no picture available and when images are acquired they need to be of a reasonable quality. FRT threshold must be set appropriately.
- the passive nature of this technology increases the number of faces that are verified - a possible terror suspect can be apprehended in a discrete manner without making a scene. - passing off the monitoring to a machine creates impartiality? since a machine is doing the verifying. (not sure is this is true or not)
3.3 Facial recognition for search realistically.
“Imagine never forgetting a face, and always being able to recognize a friend-of-a-friend on the street. And because the systems arelinked to the Internet, once AR recognized someone, you could easily pull up his or her online footprint, such as a Facebook page.”
Cascio, Jamais. "Seeing Too Much." Atlantic Monthly (10727825) 304.4 (2009): 34. Academic Search Premier. EBSCO. Web. 1 Nov. 2010.
Recorded Details: Face recognition can be used to search faces online. This technology has made possible to search a person using his image online. Just upload the image online; Hit the search; Surprisingly enough, all the publicly available information is listed. Although it is very impressive to search in such a way, there
are some security concerns. Next part will be discussing about security concerns.
4. Problems due to technology.
4.1 Facial recognition vs. privacy in social media driven world:
1. “The United States Constitution declares a level of protection for the rights of individual citizens against oppression by their government that has made the U.S. unique. The right to privacy is one right that has become a firmly entrenched American value, even though it is not explicitly enumerated in the Constitution. Since face recognition systems represent leading-edge and rapidly changing technology, this technology is currently a major issue in the area of social im- pact of technology.”
Bowyer, Kevin W. "Face Recognition Technology: Security versus Privacy." IEEE Technology & Society Magazine 23.1 (2004): 9-20. Academic Search Premier. EBSCO. Web. 1 Nov. 2010.
2. “We don‟t regard ourselves as „Big Brother‟. We‟re more like a friendly uncle or aunt watching over you.” Hale, Benjamin.
"Identity crisis: Face recognition technology and freedom of the will." Ethics, Place & Environment 8.2 (2005): 141-158. Academic Search Premier. EBSCO. Web. 1 Nov. 2010.
Recorded Detail: Social networks have become more ubiquitous in day to day business. A persons online persona is created, sometimes without the persons consent. Having the ability to query using Facial Recognition Technology creates a situation where the party doing the surveillance can do this in a passive manner. The time taken to investigate an individual and do an assessment can
be done in a quickly. Having a FRT system suggests to a society that one has to conform to norms and obey the law.
Face recognition technology Notes:
(We may use one of the quotes from the listed below)
The Privacy Argument
"The difference between what a computer might do to make the connection between two publicly available data sets (like a publicly captured faceprint and a mugshot) and what a police officer, an insurance representative, or a private agent might do is only a matter of speed."
"detractors of FRT might do better to argue that it is a restriction of freedom, and not privacy alone"
The Objective Freedom Argument “the capacity to delve into the moment- to-moment lives of citizens, the potential for abuse is tremendous”
“arbitrarily passing and enforcing laws if citizens who might otherwise object to those laws can be monitored to the degree that any dissent might be quashed before it be- comes dissent worth worrying about?”
“it holds the potential to make all laws fully enforceable, regardless of whether the laws themselves are just”
The Subjective Freedom Argument
“One need no longer will maxims to be universal laws, because the question of univer- sal law has been supplanted by the positive authority of institutional law. The categori- cal imperative, in a nearly just society of perfect enforcement, will read instead: „One ought to act according to that maxim that is the positively enforced state law‟.”
“The problem here is that the operationalization of ethical deliberation transforms the meaning of freedom altogether, shifting it from the free establishment of self-imposed rules about how to act (from autonomy) to a calculable choice between a list of exter- nally provided ends (to heteronomy).”
Recognition and Freedom
“ In the world of FRT, the institutional identity provides a surrogate practical identity, supplanting (1) the reflectively reasoned and willed adoption of normative stances about how to act with (2) a characterization of an individual‟s institutionally recognized past actions.”
“One is either a lawbreaker or a law-abider, and when one is a lawbreaker, one is a lawbreaker according to what one has already done.” (past actions determine how people society treats you)
“At the objective level, freedom is eclipsed because people are no longer able to break the law and to succeed at breaking the law.”
“With highly and heavily administered FRT, the suggestion that one „ought to obey the law‟ is transformed into the more binding mandate that „one cannot avoid obeying the law‟.”
“the idea of freedom necessarily implies that one maintain the possibility of deliberating over courses of action, of acting according to practical reason.”
“The threat to individual autonomy is therefore twofold: first, regarding possibilities for setting individual laws; and, second, regarding possibilities for the social recognition of self-determination and public establishment of autonomous agency. FRT provides the means for men to undo the progress of modernity. It makes real the previously unimag- inable prospect that free will, that darling of the Enlightenment, will henceforth be dog- ged by cameras.”
4.2 Facial recognition false positives.
“Distinctiveness of a face predicts both miss errors (MEs) and false positives (FPs) but correlations between these errors are low”
Lewis, Michael B. "Familiarity, Target Set and False Positives in Face Recognition." European Journal of Cognitive Psychology 9.4 (1997): 437-459. Academic Search Premier. EBSCO. Web. 1 Nov. 2010.
Recorded Detail:
People recognize faces that have personal familiarity this determines if false positives will occur. Face Recognition Technology in some instances produce false positives when subjects are wearing eyeglasses, lighting glare. For FRT to work a frontal view of the face is needed.