Inspiration:
Often, many students will intend to briefly check their phone for social media, but then end up scrolling on TikTok or Instagram for countless of hours, resulting in little to no work being completed. Our project aims to prevent this behaviour by alerting the user to put down their distractions, focus back on the camera frame and promote productivity as a result.
What it does:
Tracks facial recognition to ensure you are focused on a computer screen. If no facial recognition is present for more than 5 seconds, an alert will sound repeatedly until a face is back in the frame.
How we built it:
This project was implemented using the openCV (cv2) library on Python, enabling the tracking of facial recognition using a camera. An XML file from online was used to help the program mathematically determine whether or not a face was in the camera frame.
Challenges we ran into:
Often our application would mistaken background objects (i.e. lights, posters, signs) as faces, thus making it most effective in a blank background setting.
Accomplishments that we're proud of:
This was the first Hackathon for several of us and we're proud that we were able to produce a quality project that could help many people in a limited amount of time.
What we learned:
We learned how to use the openCV (cv2) library on Python to track live facial recognition
What's next for Hocus Focus:
We would like to eventually release it as a Google Chrome Extension so that at anytime, anyone will be able to use it. For demonstration and judging purposes, the camera footage is displayed on the user's screen, but it will be removed when this application is released more professionally.
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