Class Project "Gesture Recognition with Neural Networks"

This stand-along neural network project for an undergraduate or graduate artificial intelligence class relates to video-game technologies and is part of our effort to use computer games as a motivator in projects without the students having to use game engines. Neural networks are among the most important machine learning techniques and thus good candidates for a project in artificial intelligence. In this project, the students need to understand and extend an existing implementation of the back-propagation algorithm and apply it to recognizing static hand gestures in images. This project requires students to develop a deep understanding of neural networks and the back-propagation algorithm. It extends a project from Tom Mitchell's "Machine Learning" book and builds on ideas, text and code from that project (courtesy of Tom Mitchell). The project is versatile since it allows for theoretical questions and implementations. We list a variety of possible project choices, including easy and difficult questions.

This project was chosen as a Model AI Assignment by the Symposium on Educational Advances in Artificial Intelligence in 2010. If you are using this project in your class or have any questions, comments or corrections, please send us an email at skoenig@usc.edu. We will use this webpage to post updates, errata and supporting material. If there is sufficient interest, we will create sample solutions for teachers at accredited colleges and universities.

Project Text

• X. Zheng and S. Koenig. A Project on Gesture Recognition with Neural Networks for 'Introduction to Artificial Intelligence' Classes. Technical Report, Department of Computer Science, University of Southern California, Los Angeles (California), 2010. [downloadable]
  

Latex Source of Project Text

• Latex Source

Code and Data

• Code and Data

We thank Tom Mitchell for allowing us to use some of his material. We also thank all students who volunteered to provide gesture images. Some of this material is based upon work supported by the Fund for Innovative Undergraduate Teaching at the University of Southern California and the National Science Foundation under Grant No. 0350584. Any opinions, findings, and conclusions or recomendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

If you have comments on any of these papers, please send me an email! Also, please send me your papers if we have common interests.

This page was automatically created by a bibliography maintenance system that was developed as part of an undergraduate research project, advised by Sven Koenig.

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