Board 57: WIP - A Web-based Face Recognition Application for Better In-Person Learning

Shirley Qin; Jiawei Tian; Yuqi Yang; Qian Guo; Junhao Liao; Hamid S. Timorabadi

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Conference: 2023 ASEE Annual Conference & Exposition
Location: Baltimore , Maryland
Publication Date: June 25, 2023
Start Date: June 25, 2023
End Date: June 28, 2023
Conference Session: Computers in Education Division (COED) Poster Session
Tagged Division: Computers in Education Division (COED)
Page Count: 13
DOI: 10.18260/1-2--42863
Permanent URL: https://peer.asee.org/42863
Download Count: 212

Paper Authors

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Shirley Qin University of Toronto

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Shirley Qin is a fourth-year Computer Engineering student at the University of Toronto. Previously, she worked as a System Integrator in City of Toronto's Infrastructure and Coordination Unit. She is interested in software programming and user interface design. She is proficient with C, C++, and JavaScript and familiar with Intel FPGA Verilog and ARM Assembly(v7).

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Jiawei Tian University of Toronto

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Jiawei Tian is an undergraduate electrical and computer engineering student at the University of Toronto. He is interested in software programming and electrical systems. He is proficient with C/C++, Java and SQL and familiar with JavaScript, Verilog and Assembly.

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Yuqi Yang

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Qian Guo University of Toronto

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Qian Guo is a fourth year Electrical Engineering student at University of Toronto.
Previously, she worked as a Quality Analyst in SS&C Technologies. She is interested in software programming. She is proficient with C, C++ and Python and familiar with PSQL, Intel FPGA Verilog and ARM Assembly.

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Junhao Liao University of Toronto

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Junhao Liao holds a Computer Engineering Bachelor degree from University of Toronto.
Previously, he worked as a Teaching Assistant at University of Toronto. He is interested in software programming and User Experience designs. He is proficient with C, C++ and Python and familiar with JavaScript, PSQL, Intel FPGA Verilog and ARM Assembly(ARMv7-A).

Personal Website: https://junhao.ca

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Hamid S. Timorabadi University of Toronto

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Hamid Timorabadi received his B.Sc, M.A.Sc, and Ph.D. degrees in Electrical Engineering from the University of Toronto. He has worked as a project, design, and test engineer as well as a consultant to industry. His research interests include the applicati

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Abstract

WIP - A Web-based Face Recognition Application for Better In-person Learning It is widely agreed that students gain more confidence and build stronger relationships when their instructor addresses them by their names. However, it is often difficult for instructors to recognize and remember all the names of students in their lectures when the lecture size becomes relatively large. A traditional approach is to ask the students to bring a name tag. However, name tags can be tacky and difficult to manage. During the covid pandemic, educational institutions utilized web meeting technologies in place for in-person meetings. When the instructors want to interact with other participants, they can call out their names without knowing them priorly by just looking at their name tags. Using face recognition algorithms, we want to bring the convenience of web meeting technologies to in-person education environments. This abstract shall introduce the proposed solution as a Work-In-Progress (WIP). Drawing inspiration from the name tag feature, we are motivated to design a digitalized solution that relieves the pressure on instructors from remembering names. The proposed solution is a web-based application that will capture faces from videos/pictures through a User Interface that operates on web browsers and passes the data to a face recognition AI mechanism. User management systems will also be implemented to protect user privacy. With the Real-time Face Recognition Application, the course instructors can quickly recognize their students and address them by their names. This creates a more engaged and confidence-inspiring learning environment for students. We are conducting surveys and collecting feedback from instructors and students while testing our application in lectures. The students’ survey focus is on their comfortability-in-learning scale and degree of agreement on an enhanced and increased classroom learning experience. On the other hand, the focus of the instructor’s survey and feedback is on students’ engagement and their willingness to engage in asking and answering questions during lectures.

Citation
Format

Qin, S., & Tian, J., & Yang, Y., & Guo, Q., & Liao, J., & Timorabadi, H. S. (2023, June), Board 57: WIP - A Web-based Face Recognition Application for Better In-Person Learning Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42863

TY - CPAPER
AB - WIP - A Web-based Face Recognition Application for Better In-person Learning
It is widely agreed that students gain more confidence and build stronger relationships when their instructor addresses them by their names. However, it is often difficult for instructors to recognize and remember all the names of students in their lectures when the lecture size becomes relatively large. A traditional approach is to ask the students to bring a name tag. However, name tags can be tacky and difficult to manage. During the covid pandemic, educational institutions utilized web meeting technologies in place for in-person meetings. When the instructors want to interact with other participants, they can call out their names without knowing them priorly by just looking at their name tags. Using face recognition algorithms, we want to bring the convenience of web meeting technologies to in-person education environments. This abstract shall introduce the proposed solution as a Work-In-Progress (WIP).
Drawing inspiration from the name tag feature, we are motivated to design a digitalized solution that relieves the pressure on instructors from remembering names. The proposed solution is a web-based application that will capture faces from videos/pictures through a User Interface that operates on web browsers and passes the data to a face recognition AI mechanism. User management systems will also be implemented to protect user privacy. With the Real-time Face Recognition Application, the course instructors can quickly recognize their students and address them by their names. This creates a more engaged and confidence-inspiring learning environment for students.
We are conducting surveys and collecting feedback from instructors and students while testing our application in lectures. The students’ survey focus is on their comfortability-in-learning scale and degree of agreement on an enhanced and increased classroom learning experience. On the other hand, the focus of the instructor’s survey and feedback is on students’ engagement and their willingness to engage in asking and answering questions during lectures.

AU - Shirley Qin
AU - Jiawei Tian
AU - Yuqi Yang
AU - Qian Guo
AU - Junhao Liao
AU - Hamid S. Timorabadi
CY - Baltimore , Maryland
DA - 2023/06/25
PB - ASEE Conferences
TI - Board 57: WIP - A Web-based Face Recognition Application for Better In-Person Learning
UR - https://peer.asee.org/42863
DO - 10.18260/1-2--42863
ER -