Application Facial Recognition Technology -Myassignmenthelp.Com

Question: Discuss About The Application Facial Recognition Technology? Answer: Introduction Facial recognition technology (FRT) is regarded as a science fiction. In the past years this technology has become viable and widespread. Face recognition is commonly seen when reading technology news. There are various organizations that benefit from this technology. Agencies of law enforcement for instance use face recognition to provide security to the community. Retailors on the other hand prevent violence and crime. In addition, mobile phones organizations use face recognition to clients with new layers of security. In this context, FRT will be discussed based on how it came to existence, where and how it is applied, its requirements and ethical considerations. History of FRT Due to face recognition theoretical interest and practical essentiality from realistic scientist, this technology is aged as computer vision. Although other methods of recognition like iris scans and fingerprints are accurate, face recognition due to its non-obstructive nature and it being the primary method in which people use for persons identification, it remains to be the main researchers focus. (Gates, 2011) Kohonen are thought to be the first to demonstrate face recognition examples. They showed that face recognition for normalized and aligned images of the face could be performed using simple neural net. Kohonen's system due to the need for accurate normalization and alignment failed practically Most people suggest that Woodrow Wilson Bledsoe is the inventor of facial recognition. Woodrow in the 1960s built a system that could categorize face photos by hand with the use of RAND tablet (a machine used to enter vertical and horizontal coordinates on a grid with the use of electromagnetic pulses stylus). This system recorded locations of coordinate manually of several facial features like nose, mouth, eyes and hairline. DARPA (Defense Advanced Research Products Agency) sponsored FERET (Face Recognition Technology Evaluation) from 1993 to 1997. This resulted to development of technology and algorithms of face recognition by analyzing its prototype. Some of the areas of economic where facial recognition is used include; Government Use- agencies of Law Enforcement reduce traumas of victim trauma by minimizing mugshot searches, validating identity for court records, and identifying known molesters using camera images in school surveillance. Security is enhanced through control access and identifying terrorists through surveillance images. Quick progression through customs enhances immigration Commercial Use- activities of Day Care are enhanced through validation of individuals identity when they are coming to pick their children. Residential Security is enhanced through alerting homeowners on the approaching individuals. Voter verification is enhanced when politicians that are eligible are needed to validate their identity during process of voting. Through banking using ATM, the system verifies a client face quickly. It allows physical access control of doors, buildings areas, or net access. (Baldauf, Stair, 2011) What are technical requirements for FRT application in the different areas where it is applied? Requirements of Facial Recognition Technology unique faces in a watch list are detected using extra software and hardware. Minimum requirement for the system should be reviewed before configuring the enhancement for face capture for video analytics of IBM Intelligent Video Analytics. (Geetha, Ramalingam Palanivel, 2011). The following is a list that outlines the prerequisites and the minimum requirements for face recognition system: SSE(smart surveillance engine),DLE (deep learning engine), andMILS (middleware for large scale surveillance)items should meet the minimum software and hardware requirements for the system. Installing of SSE, DLE, and MILS items should be done according to the given instructions. Registration of DLE must be done. Video that is recorded should come from a configured channel to use the analytic profiles for body camera analytics. Static camera video should be in a supported video management system. Watch list database face images should be in the format of .jpg file, of enough quality and less than 4MB. Images should have face frontal view with lighting that is good, and a minimum of 80 pixels between the eyes. A proper access in watch list for the enrollment of people should be enhanced. (Al-Qatawneh, Jaber, 2015) Ethical Considerations for FRT Debate between security and privacy on Smart CCTV is about genuineness. This is because security and privacy may oppose each other. Facial recognition opponents cannot eliminate easily the benefits of security. Proponents also cannot ignore the risks they bring to civil liberties. Ways on how to settle civil liberties and security issues must therefore be made that will help decide how facial recognition will be used. A clear understanding of the importance of both security and civil liberty, power and technology dependence, and potential abuses and uses is required. (Maurer, 2016) The following are some of specific problems that are associated with facial recognition; Error- this problem happens when wrong matches occurs and can lead to harassment of innocent citizens by the police. Function creep- this is where a technology is structured for a restricted purpose but later gains unanticipated or additional functions. This can happen either through systematic abuse or institutionalized growth. Due to technology flexibility, the goal for which the system is utilized may be extended easily from identifying missing individuals and criminals to include other objectives and thus causing problems. Privacy- with facial recognition technology, most opponents think that the problems of function creep and error addresses the problem which is not the case. The use of facial recognition technology in public areas should not breach basic right to privacy regardless of occurrence of function creep or error. Conclusion In conclusion, technology of facial recognition is associated with secure applications that are very expensive. However, due to evolvement of major technologies that allows integration and increase processing power, equipment cost is reducing significantly. Various facial recognition software has now become dependable, highly accurate and cost effective. As such barriers of technology and finance are dealt with thus enhancing widespread deployment. Recommendation Prohibition on the usage of face recognition to mark people on the basis of political views, races, or ethnicity should be advocated. Agencies of law enforcement should ensure that face recognition is subjected to requirements of public reporting and internal audits. Face recognition in federal and state financial assistance should be transparent and accountable. References Al-Qatawneh, S., Jaber, K. (2015). Parallel Cascade Correlation Neural Network Methods for 3D Facial Recognition: A Preliminary Study.Journal of Computer and Communications,3, 54-62. doi:10.4236/jcc.2015.35007. Baldauf, K., Stair, R. M. (2011).Succeeding with technology: Computer system concepts for your life. Boston, Mass: Course Technology/Cengage Learning. Gates, K., New York University Press. (2011).Our biometric future: Facial recognition technology and the culture of surveillance. New York: New York University Press. Geetha, A., Ramalingam, V., Palanivel, S., (2011). "An Integrated Face Tracking and Facial Expression Recognition System,"Journal of Intelligent Learning Systems and Applications, Vol. 3 No. 4, 2011, pp. 201-208. doi:10.4236/jilsa.2011.34023. Granger, E., Defence RD Canada,, Centre for Security Science (Canada),, Canada Border Services Agency. (2014).Evaluation methodology for face recognition technology in video surveillance applications. In Bris?, R., In Majernik, J., In Pancerz, K., In Zaitseva, E. (2016).Applications of computational intelligence in biomedical technology. International Conference on Electrical and Electronics Engineering (2010- ), Zeng, D. (2012).Advances in control and communication. Berlin: Springer. Maurer, D. C. (2016).Face recognition technology: FBI should better ensure privacy and accuracy : report to the Ranking Member, Subcommittee on Privacy, Technology and the Law, Committee on the Judiciary, U.S. Senate. Washington, D.C.: United States Government Accountability Office.