Month: April 2019

This week I made modifications (using help from https://medium.com/datadriveninvestor/understanding-and-implementing-the-viola-jones-image-classification-algorithm-85621f7fe20b) to the facial detection code and retrained the model to create a strong classifier comprising of 75 features. The results were very promising. I achieved 95% accuracy and live testing produced good results. Below is sample output:

Next week, the focus will be solely on raised hand detection. Everything is almost in place and we just need to fine tune algorithms to get higher accuracies.

 

Using SVM over Kmeans led to remarkable success with recognition. We also found a better combo of PCA eigenvectors to keep. We fixed a bug in stranger detection. We implemented a live display of coefficients in the eigenspace. Facial detection is also seeing major improvements.

 

This week I pair-programed with Neeraj to implement the usage of SVM over Kmeans for recognition of the fisherface eigen coefficients. In other words, rather than trying to match a new face to the nearest centroid in the eigenspace, we partition the eigenspace into zones using SVM and label them accordingly. We also selected a more optimal selection of PCA eigenvectors (dropping only the first two). The combination of these led to remarkable success in our recognition algorithms. We are now seeing more than 90% accuracy in recognition.

Part of our success above was thanks to implementing a tool which helps us visualize where faces land in the eigenspace. I programmed a tool which displays live the location of a face in the eigenspace along with the existing camera frame visualizer. Below image displays that tool (X marks the spot of the live face representation).

I found some bugs with the cosine and E2 stranger detection metrics and fixed them.

This week I worked with Omar to get the PCA and LDA part of our facial recognition to give us good results. We merged the rewritten PCA code that I had at the start of the week with the existing LDA code. Our new results look really good. Below is a picture of the LDA output for each of the three classes (each color is a different person).

I am slightly behind schedule for the week as the main part of recognition has been finished, but we need to finish the classification part. For the final week before the presentation, we need to now integrate the rewritten recognition components with the MVP file.

 

Neeraj finished some early testing for our new fisherfaces implementation and we got very good results with that. Neeraj and Kevan are now working on combining that with our existing codebase.

 

This week me and Neeraj pair programmed to re-code our PCA. We finished that and tested and compared those results between the old PCA and new PCA in terms of the quality of the reconstructions as we varied the eigenvectors that were used. We then used our existing LDA code and interfaced that code with this new PCA in order to generate a new set of fisherfaces. Neeraj finished testing that fisherfaces and we got very good results with that. Neeraj and Kevan are now working on combining that with our existing codebase.

We are on-schedule.

The previous facial detection code was running too slow so I created a cascading classifier. The classifier works, producing accuracies of 80%. I am waiting for more AWS credit so that I can train the classifier on a larger scale with more images/features. This week was a bit slow, but I hope to catch up next week. I will work with Neeraj to integrate his PCA into the mvp script. I am waiting for waiting to train my cascading classifier before adding it to the mvp script. I will also be working on improving hand raised detection.

This week I worked on rewriting the core PCA code to check and see if there were any issues. I finished the rewrite of the PCA code during the week and it has been committed to a branch folder. The results shown by the PCA are quite similar to those achieved by Omar. Thus, I believe that the issue here should be with the LDA portion of the code. I’m working on writing the LDA code as well, but that is not complete yet.

I am on schedule for the week. I intend to have the LDA code finished early this week. The plan is to work with Omar to finalize facial recognition accuracy by the end of the week so that the final week, we simply work on polishing the mvp_prototype.py file for the demo.

 

We have fused facial detection. We tested with 256×256 sized data-set.  Neeraj is working on recreating the PCA and LDA code to verify that it works. We will soon begin working on mouth movement detection once we reach the desired accuracy for all the other components. The main issue at the moment is getting the accuracy above 70% for the facial recognition component.

 

 

This week we got our camera (I set the camera up to work with our code) and we performed some live tests to see how we would fair with better quality data. We hypothesized that 256×256 dataset would perform better and we tested on Me, Neeraj, and Kevan. Unfortunately, while the testing performance was decent the live web-cam performance was still not acceptable.

Currently, Neeraj is recreating some of the PCA and LDA to verify that those code portions are correct.

I am on schedule.