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Board 137: MAKER: Facial Feature Detection Library for Teaching Algorithm Basics in Python

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Conference

2018 ASEE Annual Conference & Exposition

Location

Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

Make It!

Tagged Division

Manufacturing

Page Count

11

Permanent URL

https://peer.asee.org/29934

Download Count

236

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Paper Authors

biography

Mehmet Ucar

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M.S. in Computer Engineering, University of Houston- Clear Lake(2016)
B.S. in Electrical and Electronics Engineering, Erciyes University (2008)

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biography

Sheng-Jen Hsieh Texas A&M University

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Dr. Sheng-Jen (“Tony”) Hsieh is a Professor in the Dwight Look College of Engineering at Texas A&M University. He holds a joint appointment with the Department of Engineering Technology and the Department of Mechanical Engineering. His research interests include engineering education, cognitive task analysis, automation, robotics and control, intelligent manufacturing system design, and micro/nano manufacturing. He is also the Director of the Rockwell Automation laboratory at Texas A&M University, a state-of-the-art facility for education and research in the areas of automation, control, and automated system integration.

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Abstract

High school students sometimes have difficulty understanding the concept of an algorithm in computer programming. This paper describes a facial feature detection library and instructional procedures to teach beginning-level programmers about algorithms. Writing programs to detect facial features such as ears, nose, and eyes can be motivating but challenging. An interactive graphical user interface was designed to simplify this task. The interface includes icons for facial features such as ears, nose, and eyes. Each icon corresponds to a set of feature detection algorithms. Student can select a feature icon, import an image, and run the program. The program will show detection results for the selected feature icon. After reviewing the results, students can open the code window of the selected feature and review and revise the code to improve the feature detection performance. Through this process, students can improve both their understanding of algorithms and their programming skills. Results suggest that the instructional module is effective and the task is enjoyable for students.

Ucar, M., & Hsieh, S. (2018, June), Board 137: MAKER: Facial Feature Detection Library for Teaching Algorithm Basics in Python Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/29934

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