Student Spotlight: Ryan René Rosado
By Suzanne Lazar ’16, graduate student, SUNY Empire State College; editor, The Student Connection
February 8, 2018
It’s exciting when an opportunity presents itself to showcase the talents of one of our very own students at SUNY Empire State College.
Ryan René Rosado is a consultant and an analyst for Ernst & Young’s Cyber Threat Intelligence (CTI) team. She is pursuing a bachelor’s degree in cybersecurity at Utica College and a bachelor’s degree in emergency management at the State University of New York (SUNY) Empire State College. She is an active member of Armed Forces Communications and Electronics Association (AFCEA) International’s Women in AFCEA committee and AFCEA’s Alamo Chapter.
Rosado recently wrote an article “Disruptive by Design: Biometrics Could ID Criminals Before They Act” for the AFCEA online magazine, Signal. The views expressed are her own. Here’s what she had to say:
“With modern society’s infatuation with selfies, facial recognition technology could easily be used to identify common physical traits of criminals, pinpoint communities dominated by potential offenders and then help determine where to focus crime-prevention programs.
Crime statistics largely dictate the locations of these programs, but a system based on facial recognition of criminal traits could offer a more proactive and effective method, among other benefits. A study comparing physical traits of prisoners using 3-D facial recognition technology could advance criminal-prevention efforts and help tailor our criminal justice programs and intelligence collection methods. We then could prevent crime and terrorism without racial profiling. Another potential benefit—albeit a controversial one—is that the U.S. military branches might be able to use the technology to weed out recruits likely to end their military careers in jail.
Facial recognition focuses on physical traits that will not change as people age. These physical traits include curves of the eye socket, nose and chin. Certain qualities to these features can be linked to potential criminals.
Two Chinese researchers announced in late 2016 that their facial recognition algorithms could identify criminals with a high degree of accuracy. New Scientist reported at the time that the researchers exploited machine learning, asking facial recognition software to guess whether a person in an ID-style picture was a criminal and then feeding it the correct answer. It learned to tell the difference, eventually achieving up to 90 percent accuracy. The magazine also reported that the study was highly criticized by other researchers, in part, because the photos of criminals and non-criminals came from two different sources, which may have influenced the software.” Read more.
This article was originally published in Signal Magazine for AFCEA.
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