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iDetect
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iDetect metrics
Inspiration
With online platforms becoming a more mainstream means of meeting we wanted to create a way for speakers to be able to determine the satisfaction of their audience even when they are unable to see the audience
What it does
The application streams the video of the audience and analyzes their faces to determine their emotions, the speaker will be able to see what percentage of their audience is happy, sad, angry, or neutral
How we built it
iDetect uses python's deepface library to recognize faces and outputs the audience's emotional statistics on a table and a graph that shows changes in emotion over time
Challenges we ran into
Deciding on a facial recognition library was challenging because there were so many and we needed one that was simple to use yet complex enough to classify emotions
Accomplishments that we're proud of
Creating a working program that uses image processing
What we learned
How to use the deepface library of python to run facial analysis on images
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