FaceCheck

Inspiration

I realized that due to the Covid-19 pandemic, many teachers have migrated to digital or hybrid learning, but because of these environments they don't know how well-received their lectures would be by the students. I wanted to create a program that would allow the teachers to determine how their lectures were received by the students while also making sure that students were more likely to pay attention during lecture.

What it does

The app if able to look at the face of the person being recorded and tell what emotion, that they are displaying. From there the app will then be able to determine how long certain emotions persisted during the video lecture. Furthermore, the app also tracks the eyes of the student in order to determine if they are still paying to the lecture. If the eyes are not located the system will chime to get the student back on track.

How we built it

The program was built using my knowledge of python, and my understanding of existing libraries out there that would be able to determine the emotions being displayed. Then I used python commands that were specific to Computer Vision to generate bounds around the eyes to determine whether or not the person was awake or asleep.

What's next for FaceCheck

I wasn't able to complete the summary report, so the next step for FaceCheck would be to have summaries generated for the teacher in order for them to see how their lectures were received by the students. Then I plan on adding features in the program that would allow the teacher to host activities while lecture is going on.

Built With

• python
• xml