This is a face detector built for a class on computer vision. It uses HOG features to train an SVM, both of which are provided through the VLFeat library. It then uses a sliding window function for face detection on larger images, preventing overlapping boxes through mean IoU and non-max suppression. Multi-scale detection is implemented across 3 scales.
The dataset originally was composed of approximately 14,000 36x36 images evenly split between faces and non-faces. I added data augmentation in the form of left-right flipping, up-down flipping, intensity scaling, and addition of noise. The scripts for augmenting the dataset are in generate_faces.m and generate_cropped_notfaces.m.
The SVM can be trained on HOG features using train_svm. The SVM uses Stochastic Gradient Descent as its solver with L1 regularization. Cross-fold validation is done for training the SVM and tuning the lambda hyper-parameter. The final SVM is stored as mysvm.mat.
For running the detector, we use a sliding window function to establish candidate boxes in the image. The image is taken at its original scale as well as half and double scales to ensure detection is multi-scale. Image scales can be added as needed in detect. To avoid overlapping detections we use mean IoU and suppress non-maximum responses.
To run, first load the SVM weights with load('mysvm.mat')
To run on an example image provided in this repo, run detect_class.
In general, you can run detect, as long as the imageDir variable in detect.m is set to the correct one.
