Facial+Recognition+Systems

= What Is Facial Recognition? = toc Facial recognition is the act of matching an image of an unidentified person to an image of a known person. This process has within the past few decades been significantly enhanced by technology, especially that of 3-D imaging systems. It can, however, range from being a complex calculation of algorithms to a simple act of anyone tagging a person they are familiar with in image software. Consequently, its uses range from enhancing security measures to social networking.

While facial-recognition in social networking such as Facebook relies on a less-technical way of users identifying people manually, the more technical facial-recognition systems rely heavily on computer technologies, specifically that of imaging systems and databases. With developments in cameras and databases, facial-recognition systems and software will continue to improve. Indeed, government funding and corporate ventures have resulted in a number of companies establishing their business around the technology.

More advanced facial recognition technology requires several steps. First, an image of a face has to be captured. Second, the orientation of the face in the image must be aligned. Once the face’s orientation is properly aligned, the facial recognition software then makes a template from measurements between features of the face. These measurements are made between 80 distinct points on the face. These points are the key peaks and valleys of a facial skeleton that never change. After that, a computer translates the template into digital code. Each person’s face would have a unique digital code due to inherent differences in their facial structure. Facial recognition is achieved when an image in question is matched to an image in a database.

A less complex kind of facial recognition is exemplified by Facebook. Users can simply tag people they know, and the information is subsequently stored in the database of information about that person. Some have speculated that Facebook could use the information that people have built up from tagging others to eventually allow people to photograph someone, upload the photo to Facebook, and subsequently identify that person without any prior association. While this could help to bring people closer together, some consider it a potential threat to privacy.

How Do Facial Recognition Systems Relate To Web Technologies And Information Professionals?
For facial recognition software to be successful, it must match images on a one-to-one basis. The key to this matchup is a database. With a relational database, for instance, an SQL query could be run through the database’s list of unique codes representing people’s faces and retrieve the one that matches that of the facial code in question. Information professionals with proper training and skills are needed to maintain these databases and accompanying software.

What Are Some Examples of Facial Recognition's Use Or Possibilities?
Facial recognition technologies are used largely to prevent passport fraud and identity theft, support law enforcement, and identify missing persons, among other security purposes. However, other ways in which it has been used include software programs such as Google’s Picasa, Apple’s iPhoto, and even Facebook. The ways in which facial recognition is achieved for each of these varies.

Commercial vendors have even made products utilizing facial-recognition to safeguard your personal computer. The company KeyLemon relies on a computer’s webcam to recognize the computer owner, assuring that no one else who sits in front of the computer is allowed access to use it. The company has even suggested its possible application to videos. Use of such technology in an information center could allow employees to feel more at ease should they need to step away from their computer. media type="youtube" key="8TkVx8u9-Pk" height="315" width="560"

Facial recognition systems could be used to facilitate many processes. Libraries could implement such technology to immediately recognize registered patrons as they walk in. With other systems in place like RFID tags in books, users could by-pass visiting a check-out kiosk since the facial-recognition system could register the patron, and the RFID tag could register the book. Checking out books and media from the library could eventually become a near seamless process.

Facial Recognition Systems and Information Professionals
Because facial recognition systems rely on databases of image files, information professionals are highly relevant to working for groups or institutions that use the technology. The databases need to be maintained and monitored, since without the images to relate to, facial recognition system would be useless. Various institutions have mandated policies and best practices guidelines to follow, and the following are some examples for any information professionals aspiring to take such a technical route:

"Best Practices for Database Maintenance" from IBM @http://www-10.lotus.com/ldd/dominowiki.nsf/dx/best-practices-for-database-maintenance This provides a brief example of protocols to follow, and is a quick introduction to begin thinking about the maintenance work involved in running databases.

"Oracle Configuration Best Practices" from Stanford University @http://www.stanford.edu/dept/itss/docs/oracle/10g/server.101/b10726/configbp.htm This is a far more substantial chapter containing information about how to best manage an Oracle database, offered from Stanford University.

**References**
Baird, C. (2004, June). Oracle Configuration Best Practices. In //Oracle® Database High Availability Architecture and Best Practices// (7). Retrieved from @http://www.stanford.edu/dept/itss/docs/oracle/10g/server.101/b10726/configbp.htm

Blender Foundation. (2009, August 1). Start importantLoops [Photograph]. Retrieved November 13, 2011, from @http://commons.wikimedia.org/wiki/File%3AStart_importantLoops.png

Bonsor, K., & Johnson, R. (n.d.). How facial recognition systems work. Retrieved October 18, 2011, from @http://electronics.howstuffworks.com/gadgets/high-tech-gadgets/facial-recognition.htm

Doyle, S. (2011, January 13). Best Practices for Database Maintenance. In //IBM: Lotus Notes and Domino Wiki.// Retrieved from @http://www-10.lotus.com/ldd/dominowiki.nsf/dx/best-practices-for-database-maintenance

//Facial recognition//. (2011). Retrieved October 18, 2011 from @http://www.findbiometrics.com/facial-recognition/

Geuss, M. (2011, April 26). Facebook facial recognition: Its quiet rise and dangerous future. Retrieved October 18, 2011, from @http://www.pcworld.com/article/226228/facebook_facial_recognition_its_quiet_rise_and_dangerous_future.html

Introna, Lucas D., & Nissenbaum, H. (2009, April 8). Facial recognition technology: A survey of policy and implementation issues. Retrieved October 18, 2011, from @http://www.nyu.edu/ccpr/pubs/

//KeyLemon// [Home Page]. (2010). Retrieved November 9, 2011, from @http://www.keylemon.com/

KeyLemonSoftware. (2010, February 25). KeyLemon presentation [Video file]. Retrieved November 13, 2011, from @http://www.youtube.com/watch?v=8TkVx8u9-Pk&feature=related

Smith, K., Ross, A., Colbry, D., et. al. (2006, August 7). Face Recognition. Retrieved October 18, 2011, from @http://www.biometrics.gov/ReferenceRoom/Introduction.aspx

Williams, M. (2007, May 30). Better face-recognition software. Retrieved October 18, 2011, from @http://www.technologyreview.com/computing/18796/page1/