This week, we continued to work on implementing our respective portions of the project.
Jessica continued to work on the facial detection portion, specifically looking into the saving of videos, the alerts, and the facial landmark part. She was able to get the video saving portion to work, where the VideoWriter class in OpenCV is used to write video frames to an output file. There are currently two options that exist for the alerts, one with audio and one with visuals. Both are able to capture the user’s attention when their eyes are off-centered. She began looking into the facial landmark detection and has a working baseline. Next week, she is hoping to get the center of the nose and mouth coordinates from the facial landmark detection to use as frames of reference for screen alignment. She is also hoping to do more testing for the eye detection portion.
Shilika worked on the signal processing algorithm and has an input ready for the neural network. She followed the process of applying a pre-emphasis, framing, windowing, and applying a fourier transform and power spectrum to transform the signal into the frequency domain.
Mohini also continued to work on the signal processing algorithm. The decision the team has made in regards to categorizing entire words, rather than individual letters, reduces many of our anticipated risks. The signals of many of the individual letters were looking quite alike whereas the signals of the different words have distinct differences. This change will simplify our decision greatly and is expected to have a higher accuracy as well. Next steps for Mohini include feeding the signal processing output into the neural network and fine tuning that algorithm.