In this project we implemented a program to do the face-detection.
The face detection mainly contains the following steps:
The positive training database of 6,713 cropped 36x36 faces from Caltech Web Faces project, and negative training database come from Wu et al. and the SUN scene database.
For each negative images, we randomly generate two integers, indicating the position of the 36x36 images. And the total number of random negative samples is 300,000.
We use Linear SVM(vl_svmtrain) with lambda as 0.0001 to get a linear classifier. The visualization of the trained classifier is shown below
We change the scale of window by zooming the original test picture, downsampled to 90% recursively, and not stop downsampling until the picture has size less then 36x36. The step size we decided is 4, in compromising the efficiency and accuracy.
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HOG Visualization
Precision Recall curve for the starter code.
Viola Jones
Visualizing SVM
Example of detection on the test set from the starter code.