This week I focused on integration and facial recognition. For integration, I worked with Arnav to understand the frontend and backend code. I now have a strong understanding of how the distraction data is sent to our custom API so that they can be displayed in the webpage. Now, sleep, yawning, gaze, phone, and other people detection are integrated into the frontend and backend.
I also worked on splitting calibration and distraction detection into separate scripts. This way, calibration data is saved to a file so that it can be retrieved when the user actually begins the work session and so it can be used in future sessions. I updated the backend so that the calibration script is triggered when the user navigates to the calibration page when starting a new session. After calibration is complete, the user will then click the finished calibration button which will trigger the distraction detection script.
After the initial testing of different facial recongition models, I have began implementation of the facial recognition module for our app. So far, the script will run facial recognition on the detected faces and print to the terminal if the user was recognized and the timestamp. The recognition runs around every one second, but this may need to be modified to improve performance.
I also began testing that the distraction detection works on users other than myself. Sleep, yawn, and gaze detection have performed very well on Arnav and Rohan, but we are running into some issues getting phone detection to work on Arnav and Rohan’s computers. I will investigate this issue in the following week.
Overall, my progress is on track. Although I did not finish implementing facial recognition, I have got a good start and was able to focus on integration in preparation for the interim demo.
Facial recognition output and screenshot:
User recognized: 21:55:37 User recognized: 21:55:38 User recognized: 21:55:39 User not recognized: 21:55:40 User not recognized: 21:55:41 User not recognized: 21:55:49
