https://github.com/kevinknights29/computer-vision---fisc-hackathon-2021
This project focuses on the usage of OpenCV and Azure Face-API to detect and recognize students, while also logging the students reactions during class.
https://github.com/kevinknights29/computer-vision---fisc-hackathon-2021
computer-vision
Last synced: 7 months ago
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This project focuses on the usage of OpenCV and Azure Face-API to detect and recognize students, while also logging the students reactions during class.
- Host: GitHub
- URL: https://github.com/kevinknights29/computer-vision---fisc-hackathon-2021
- Owner: kevinknights29
- Created: 2022-01-02T07:17:51.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2022-01-02T07:17:53.000Z (almost 4 years ago)
- Last Synced: 2025-01-27T23:46:58.633Z (9 months ago)
- Topics: computer-vision
- Language: Jupyter Notebook
- Homepage:
- Size: 995 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Computer-Vision---FISC-Hackathon-2021
### NOTE: This repository only contains the integration of Azure Face-API service with OpenCV, and the data analysis performed, which was all developed by me (Kevin K.) as the Machine Learning Lead.
This project was developed as part of the UTP - FISC Hackathon 2021, and got the second place above more than 25 projects.
ARPANET is a solution focused on guaranteeing the participation and improving the interaction of the participants of a virtual session with the help of Facial Recognition using Azure services (Microsoft cloud service), emotion analytics and interactions in class, and PowerApps to create an application that can be included within Microsoft Teams or used on a mobile device.
# Objective:
- Guarantee the student participation in class using computer vision tools and techniques.
- Measure the student reactions during the class using computer vision tools and techniques.
- Generate an analytics report to help students and professor improve their class interaction.
- Improve the quality of the teaching in class with the objectives mentioned above.
# Dataset Description:
The dataset used to recognize the student face was a set pictures taken from the UTP's student page (students' id picture).
The reaction dataset was generated after the student's face is recognized using FACE API expression analysis to log the reaction and student id.