In this project, we aim to use methods learned in class to apply supervised machine learning models, as well as computer vision concepts and methods, to develop a functional face detection model. More specifically, we train a Soft-Margin Support Vector Machine using HoG (Histogram of Oriented Gradients) features to classify images as faces or non-faces, and then employ a sliding-window methodology to detect and locate faces in a new image.
The demo video may be found at http://youtu.be/9mA_VJ2cCtU
Note: the demo video uses trainSVM.m and classDemo.svm independently for time purposes. To perform the demo on your own, we recommend cloning this repository and using FINALDEMOSCRIPT.m instead
A note on running the demo. The data matrix is 498x60000, so the demo takes a lot of memory to train, sometimes reaching 27Gb while computing the subgradient (Hessian). If this will not run on your computer, you may load the final coefficients by using load('finalCoeff.mat'), then simply comment out the softSVM() function call in the demo script and execute the script. If you are fitting the model, please note that the demo will take some time to run.
Finally, a note on licensing: this project uses the VLFeat package:
VLFeat is distributed under the BSD license:
Copyright (C) 2007-11, Andrea Vedaldi and Brian Fulkerson Copyright (C) 2012-13, The VLFeat Team All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.