What we have done:
Dylan has changed some of the face detection functions so that they are easier for the other group members to use. Previously things like blacking out the eyes was solely done in a script, but now other members just need to call the function. Dylan has also worked on dewarping the face, after it is warped for the facial recognition. There seems to be a problem with the translation that takes place with the affine function. Though even without the translation, there is a lot of blurriness in the dewarped face. Dylan has also prepared for projecting images for once the projector arrives to him.
Claudia has worked closely with Dylan and helped with many of the tasks listed above.
Avi is ready to start implementing different neural network architectures. He recently got facial classification to work with 94% accuracy for 6 different people. This means that it will be possible to train the adversarial network. Avi has also finished setting up the classifier to be easy to use for Dylan and Claudia.
What we are planning to do:
Dylan is currently blocked on using the projector. It is being shipped to him, but he can’t really test any of the projections until the projector arrives. Dylan plans on finishing the warping and hopefully projecting images accurately onto faces. The projecting onto faces is already partially complete, but need to find how well every part of face is projected onto to see if a more intense homography estimation is necessary.
Avi will implement one of the planned adversarial network structures in the next week, and set up the ability to train and characterize it. This will make it easy to spend the next week trying multiple different architectures using the existing setup. Right now, all testing will be with digitally produced changes to photographs. Due to the separation of our group, Avi will be testing with only these digital changes (not the projector) for the next few weeks.
Claudia is going to start editing photos in Photoshop so that she can observe the effect on the classifier output. She is going to start building an intuition for what changes are effective at reducing classification rates. She will then try to directly program changes that are effective. This is a new plan by our group, which will allow us to have more independent roles which give us two approaches at our main problem (which reduces risk). We will also be able to meaningfully compare the successes of the machine learning approach versus the human learned approach.