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Using Biometrics to Track Student Use of Academic Support Services

Undergraduate #352
Discipline: Technology and Engineering
Subcategory: Computer Science & Information Systems

Sadat Sayon - Onondaga Community College
Co-Author(s): Vicentica Valdes, Onondaga Community College, Syracuse, NY



The Collegiate Science and Technology Entry Program at Onondaga Community College (CSTEP) provides student support services such as counseling, academic coaching and tutoring. To optimally allocate resources, CSTEP tracks the students and the services that they use. Some of the issues with accurately tracking student use of services include students forgetting to sign in and/or out because they did not see the sign-in book or they were in a rush or sometimes just forgetting. To help with the tracking of students and because of CSTEP’s technological focus, we decided to look at the use of biometrics to track student usage of support services. Biometrics is the science and technology of measuring and statistically analyzing biological data. Biological data is measurements of human body characteristics such as fingerprints, eye retinas and irises, voice patterns, facial patterns, and hand measurements. Because many of us are ‘on camera’ as college students and as the CSTEP/LSAMP Office, we decided to study facial recognition biometrics to help us track student service use. Facial recognition allowed us to develop a database that did not store more personal, security-related information such as fingerprints and eye retina information more in line with law enforcement and banking use. An investigation of the use of MATLAB facial recognition software led us to develop a test database to consider the feasibility of tracking the use of student support services. A procedure extracting and storing features using CSTEP staff members and the MATLAB Computer Vision System Toolbox is presented. Additional work to optimize coding by investigating the use of different MATLAB extraction functions as well as the impact of image file types is under way.

Funder Acknowledgement(s): This work was supported, in part, by a grant from NSF/LSAMP to the Upstate LSAMP Alliance, Syracuse University, Syraucse, NY, 13244. This work was also supported by Mercy Works, Inc. through the Synergy Leadership Training Program, Clarence Jordan Vision Center, Syracuse, NY, 13205.

Faculty Advisor: Vicentica Valdes, v.s.valdes@sunyocc.edu

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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