Updates: Received AWS credits, ordered parts (RPi and camera). There are no changes to the design or schedule. The team is working on individual components (writing algorithms, writing app, etc.) There are no significant risks we can think of at this time; however we did discuss the possibility of storing Read more…
This week I worked on the web application for our project. I set up the application using Django, and set up the database using mySQL. The two objects we have right now are emotion and entry; “emotion” will be used to categorize the emotions of the journal entries as well Read more…
Summary: Acquired datasets to train Worked on Design Review Presentation Decided on training algorithm Changes: We decided to use pre-trained convolutional neural networks to increase accuracy and reduce the time required in the training process. We will still be training the neural networks ourselves using the SVM model. Our schedule Read more…
This week I worked on the block diagrams for the Design Review presentation including the interactions between the servers and E-MO, the IoT devices and E-MO, and the url routing diagrams between the trained neural nets, the web application, and E-MO. (The main block digram for the server and E-MO Read more…
Completed tasks: Installed Python3 and OpenCV on local machine. Installed Tensorflow Wrote face detection program Planned out algorithm for training facial emotion recognition Use pre-trained CNN (VGG19) to extract facial features VGG19 available in tensorflow Feed feature vector into SVM and classify into 6 emotions To-do: Verify face detection – Read more…
I’ve finished setting up Python/OpenCV on my local machine and polished our block diagrams. I’ve also acquired the necessary data from the AffectNet and Cohn-Kahnade databases. The AffectNet database provides around 500,000 (~120gb of data) faces categorized into 11 emotions: The Cohn-Kahnade database is significantly smaller and also dated. Thus, Read more…