Use of Technology in Criminal Justice
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Use of Technology in Criminal Investigation As the need for high-level security rises, technology usage is bound to fill up the needs in the field of criminal justice. Any innovation should not be complicated to the users so as to spread worldwide. Use of biometrics is the upcoming field in criminal justice where use of physiology is used to recognize a person person. The systems used in biometrics are fingerprints, ear geometry, voice, hand geometry, and face. Among all these systems, face recognition approach is particularly attractive. Facial detection technology is a computer application system that identifies or verifies a person from a digital video or an image from a video source. Facial recognition has begun to move to the forefront because of its purported advantages along numerous key dimensions. A person’s identity was used in domains like PINs, smart cards, passwords keys etc. which can either be easily forgotten, stolen, duplicated, misplaced, corrupted of unreadable. This prompted the use of an alternative identification module more reliable and secure. Face recognition appears to offer much better advantages compared to other forms of biometrics.
Automated biometric face recognition technology is a relatively new field. Humans have been using face to recognize faces of individuals, which is the societal most basic use. With the advancement of computer technology in the past few years has enabled similar recognitions automatically prompting major advancements that have propelled the technology further.
Since the dreaded September 11, 2001 terrorist attack in the United States facial recognition has gained enormous momentum in law enforcement to identify criminals. Currently in the U.S, the Federal Bureau of Investigation (FBI) uses Next generation identification program (NGI) which applies use of a variety of biometrics which include the use of facial recognition technology.
Face recognition technology was developed first in the 1960’s which was a semi-automated system. The system required the user to locate features such as ears, eyes, nose and mouth on a photograph then calculating ratios and distances is then compared to a common reference point of reference data. In 1970’s, 21 specific subjective markers e.g. lip thickness, hair color was used to automate recognition. The two earliest use depended on manual computation. In 1988, Kirby and Sirovich applied principle component analysis, as a standard linear algebraic technique to the face recognition technique that showed less than one hundred values were required to code successfully a normal face. In 1991, Turk and Pentland found out that Eigen face techniques that could be used to detect faces in images. This discovery gave way to automated face detection technologies that are used today. In 1997, a group of students from University of Bochum in Germany developed a software called ZN-Face that was used by Deutsche Bank and airport operators. By about January 2007, face detection used the principle of ‘text surrounding a photo’. Facelt, a software that was developed by a company in Minnesota was able to pick a face from a crowd and use database worldwide to compare and recognize. Since then up to date, a lot of improvements have been developed by use of high resolution cameras and 3D face recognition.
In modern times, face recognition is used in various fields. First, it is used in security of buildings, airports, ATM machines and seaports. It is also used in CCTV cameras for surveillance which can look for drug offenders, known criminals etc. It is also widely used in general identity verification in areas like national IDs, driver’s license, banking, electoral registration and employee cards. In ‘Smart Cards’, face recognition is used where the face-print is stored in a smartcard. In addition, it is widely used in in criminal justice systems. In the military, the U.S Navy is reported to use Robocop-style glasses fitted with a tiny camera that captures 400 images per second and use it to compare with 13million faces in the central computer database.
Despite the numerous advantages of this technology, face detection technology has had its weaknesses. The image quality affects facial recognition algorithms because the image quality of the scanning video is usually low compared to digital camera. Image size also affects face recognition when the face-detection algorithm captures an image or a video capture, the size of that face compared with the enrolled image size affects the quality of the face to be recognized. The angle that a face faces influences its recognition. Most faces in the software are frontal view; anything less than it affects the generation of the face template. Matching results are of a higher score if the image is direct and of higher resolution. Furthermore, processing and storage of every image or video is a huge task as it occupies large amounts of disc space. A cluster of computers can be used by agencies to minimize processing time. Also, the technology is hindered by people who wear glasses, masks, long hair etc. which makes it impossible to accurately identify faces. Cost implication of these technology has affected its widespread use because is quite costly to purchase, install and maintain then equipment. The good news is that with the advancement of technology, it is expected that its price will go down.
In January 2001 during Super bowl XXXV, police in Florida; Tampa Bay used facial recognition technology software to identify potential terrorists and criminals who were attending the event. A total of 19 people were identified where had minor criminal records. The police had 1,700 pictures of criminals who were being sought. The officers were impressed by the technology the police spokesman said they may consider buying the technology. Although it was not 100 percent sure, the officers were confident that they were correct matches which was to be taken to the next level.
The face recognition technology in use today has a lot of limitations which only works in constrained conditions such as front-shot images, constant lighting etc. the possible application of this technology in the future, I believe, lies in retailing. This means that in retail stores, cameras should be used as a primary means of identifying a customer and purchasing goods without using cash nor debit cards. The face recognition system would verify the identity of the customer and the total amount of sale would be deducted from the customer’s bank account. This could be extended to other areas like restaurants, retail stores, hotels and even movie theatres. Also, the technology is being used in mobile phones to unlock Android smartphones. It is just a beginning and in the future the face security feature can include a smile, a wink or a stuck-out tongue. Furthermore, the technology in the future is expected to include full body recognition which could improve the accuracy of recognition in addition to facial recognition. (Torgovnick, 2013). The use of this technology today will grow in the future. Police officers will be able to use it in patrols and in justice to solving numerous cases.
In the past 25 years, face detection technology has greatly improved. Today, machines are used to automatically verify the identity information for surveillance, secure transactions and access control to buildings. To get high recognition accuracy, computers must be used to reliably people. The evolution of this technology has come with its advantages that its cost is going down, becoming more reliable and accurate. For now, many people have accepted the use of facial recognition technology for security purpose and public safety.
References

Betz, C. (2011, December 29). Facial Recognition and Detection Technology: The Future is Now. Retrieved from The Intelligent Solutions Blog: http://solutions.wolterskluwer.com/blog/2011/12/facial-recognition-and-detection-technology-the-future-is-now/
Dargham, J. A.-M. (2012). Fusing Facial Features for Face Recognition. International Journal of Interactive Multimedia and Artificial Intelligence.
Goldstein A.J., H. L. (1971, May). Identification of Human Faces. IEEE.
Rivera, J. (2013, December 17). PoliceOne.com. Retrieved from Fcial Recognition Technology: http://www.policeone.com/police-products/police-technology/articles/6678210-Facial-recognition-technology-Staring-down-the-future/
Sirovich L., K. M. (1988). A Low-Dimention procedure for the Characterization of Human Faces. Am: J. Optical Society.
Susan, C., Rhea, D., & Brian, W. (1980). Development of face recognition: A maturational component? 257-269.
Torgovnick, K. (2013, October 17). The future of facial recognition. Retrieved from http://blog.ted.com/the-future-of-facial-recognition-7-fascinating-facts/
Zhao W., C. R. (2003). Face Race Recognition: A Literature Survey. 399-458.