In this project we implemented a sliding window face detector based on Dalal and Triggs paper from 2005. This project consisted of 4 main steps.
To see more specifics of this assignment please see the project description here
We gathered as many NxN (6x6 default) groups of positive hog features as possible, and 10000 negative hog features.
From these features (groups of 36 hog features) we constructed SVMs to discriminate between faces/non-faces.
For each test images we follow the following procedure.
All of these results are from using hog cells of size 6 and a 36x36 size template. As you can see, our face template looks (kind of) like a face. This gives us a little bit of insight into how the features are judged as part of a face or not.
Precision Recall curve.
Example of detection on the test set from the starter code.
The same image but through our final detector.