Author: ngodbole

18500_Final_Report_TEAMCA

This week was spent getting the project ready for the demo and testing the mvp_prototype constantly. We experimented and fine tuned many of the parameters in our implementation to find what gives us the best results. We also collected many new sets of data, including new sets for stranger data and much higher quality recognition training images from ourselves.

At this point, things are on schedule as it is the end and we have a working mvp, and since it is the last week there are no deliverables in the up coming week except for the report.

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.

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.

This week I updated some of the preprocessing to adjust our preprocessed image outputs since we weren’t observing accuracy improvements. For now, I have also updated some of the training images in a separate branch of the project, removing all images where the subject is not looking at the camera as we believe this is hampering our accuracy.

Mainly I have been working on rewriting the PCA and LDA code in a separate branch, making it simpler and primarily to see if the rewrite cures any of the issues present. I also feel that I can probably integrate my preprocessing into the new code better.

I am on schedule for the week as our updated schedule involves working on enhancing recognition accuracy for these two weeks (last week and this one). I feel that with this rewritten PCA/LDA, we can get the accuracy improvements necessary.

I expect to have the PCA/LDA rewrite done by Monday night, or Tuesday, and following that I want to try some additional preprocessing methods that I have found in research papers that might improve our accuracy.

This week I worked on face preprocessing code, modifying the alignment and cropping code to improve the preprocessing of the face and get only the face and with no background. I added more functions to the general preprocessing class and we have integrated it with the facial recognition. There was also a performance error last week that was causing the integrated face alignment code to run very slowly. I fixed the performance error and now the preprocessing is nearly instantaneous. I also changed the code so that all training images are preprocessed at the start as opposed to one by one. For testing, we will test images as they are sent for recognition, i.e. one-by-one. This code has been commited to the repository,

I worked with the camera and we deemed that it is not ideal for what we want as it requires making http requests and we wanted a direct stream to opencv. Omar has ordered a 1080p USB camera that we will be using. We feel that it should be suitable for our project purposes since our test conditions involve a small number of people sitting in one row.

I am behind schedule but since work in other classes has relatively freed up now, I will be dedicating much more time to capstone work. In the coming week I will be working to improve the facial recognition performance following our demo. Currently my code uses dlib face detection and that will be replaced with Kevan’s face detection which will be modified in the coming week. I will also be starting work on hand-raise detection next week.

What did you personally accomplish this week on the project?

This week I worked on researching and implementing eye alignment code that is responsible for making sure the image is aligned correctly. I am doing this using the following tutorial: https://www.pyimagesearch.com/2017/05/22/face-alignment-with-opencv-and-python/

The eye detection code is implemented using opencv and dlib. The algorithm here essentially finds the positions of the eyes and then computes the algorithm from the horizontal and position from the center, from which we obtain the affine transformation matrix that we can use to rotate the image in OpenCV. I am currently working on this code and I will commit this code to the group github repository around Sunday. I have also spent time reviewing the current PCA and LDA code in the repository to find out where we can obtain accuracy improvements as the results currently suffer from overfitting.

We also had to spend time on the Design Review Report as well as preparation for the presentation.

Is your progress on schedule or behind?  What deliverables do you hope to complete in the next week?

This week I am on schedule assuming the working code is committed on Sunday (3/4/19). For the next week I will be committing all of my time to working on the PCA and LDA code in the repository with Omar, with the goal of improving accuracy results. Deliverables for the next week would be more commits to the github repo with improved accuracy over this week. Hopefully the eye alignment code can help to improve the accuracy of the current result.