Author: kdodhia

One significant risk the group face is not being able to increase our recognition accuracy to the required MVP levels. Local PCA was not successful, so we switched to Global PCA. That still was not good enough, so we implemented LDA, used more data, and more eigenvectors. Still now, we do not have enough accuracy. We are hoping and hypothesizing that we made a mistake with the fisherfaces implementation, which when fixed will increase our accuracy.

Another risk is that we need to come up with an algorithm to delineate between a stranger and non-stranger. Two approaches have already been field with limited success.

No major changes have been made to the existing design, and the schedule does not need to be updated from last week.

• What did you personally accomplish this week on the project?

This week I continued my work on the facial detection code. Initially, I was attempting to get the facial detection to work using openCV’s pre-trained weights, however, I found that all the images were getting rejected within the first few stages. Instead of spending too much time on this, I decided to move on and go back to writing my own AdaBoost classifier. Most of the code has been written. I also spent a decent amount of time working on the Design Review Report as well as preparation for my presentation. Later in the week, I worked on incorporating feedback from the presentation into the Design Review. In particular, I expanded on our methods for hand-raised detection as the level of detail for the presentation was not sufficient.

• Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?

I think I am on schedule to fully implement a working version of the facial detection by spring break.  I was hoping to have more tangible results by now, but I think I am really close. The code has all been written and at this point I am debugging and stepping through my code to make sure I am following the algorithm properly. I hope to have a demo-able version my the meeting on Monday (3/4/2019).

• What deliverables do you hope to complete in the next week?

I will continue working on the facial detection code. I need to have a working version (that has >50% accuracy) by early next week, so that I work on optimizing and improving the detection.

Design Review ppt

Proposal Presentation_ Team CA

Name: Kevan Dodhia

• What did you personally accomplish this week on the project?

This week I implemented a facial detection algorithm that uses openCV’s pre-trained Haar Cascades. While it is not working completely yet (there are some bugs), the code is able to correctly read the Haar Cascade XML file provided by openCV, pre-process the images and calculate the integral image (and sum regions).

I also spent a decent amount of time researching possible approaches for the raised-hand detection in preparation for the design review. While there were various purely vision based approaches that looked interesting, I found, read and understood a paper that used a novel technique that we will attempt to implement:
https://ijeir.org/administrator/components/com_jresearch/files/publications/IJEIR_835_Final.pdf

• Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?

The facial detection is taking longer than anticipated. The adaboost training and cascading code is rather complicated to write. However, I have made some progress and am understanding the algorithms much more. I should be able to fully implement and test them before spring break.

• What deliverables do you hope to complete in the next week?

I will continue working on the facial detection algorithms.  I should be able to fully implement the version that uses openCV’s pre-trained Haar Cascades in by mid-week, and then work on the adaboost training code. I am on track to finish the facial detection before spring break.