Student: Scott SannerEmail:   ssanner@cs.stanford.eduCourse:  CS223B, WinterFinal Project: Rowley-Beluja-Kanade Face DetectorSystem Requirements===================Matlab 5.x with the Image Processing and Neural Nettoolboxes.Unpacking the files===================Create a directory for the project and untar the tarfileusing the command 'tar -xvf fd.tar'.  This should createa number of m-files in the current directory and asubdirectory of images used to train the neural net.Running the Face Detector=========================To build a trained, face-detection neural net, simplyrun the 'facetrain' script.  The two global variables neededfrom this script are 'NET' which is the trained neural net,and 'MASK' which is the mask used to define the area insidea rectangle which will be tested for facehood.To run the face-detector on an image using the 'facescan'function, simply pass 'NET', 'MASK', a double array of the grayscale image, and a few parameters governing the detection threshold and image scanning characteristics.  Check the 'facescan.m' file for more information on goodvalues for the paramters.File Listing============M-Files (Main Files)--------------------facetrain.m     - The main neural net training script                  which loads all required training files,                  builds the needed data structures,                  and trains and tests the neural netfacescan.m      - The main image face-scanning function.                  Simply pass this function the neural                  net, image, mask, and a few parameters                  governing the detection threshold and                  scanning characteristics.  See the                  contents of this file for for good                  default values for the parameters.M-Files (Neural Net Utilities)------------------------------createnn.m       - Creates a neural net given the input,                   hidden, and output unit characteristics.simnn.m          - Simple formats the data for presentation                   to the neural net.trainnn.m        - Trains a neural net given labeled data                   and a percentage to use for the                    validation set (which it constructs).classifynn.m     - Normalizes an image and returns the                   classification value from the neural net.M-Files (Image Utilities)-------------------------buildimvector.m  - Builds an image vector from a rectangular                   image array.  Used to convert data so                   that it can be used by the neural net                   for training.buildresvector.m - Builds a result vector to match the                   image vector.  buildmask.m      - Builds a rectangulary binary mask array                   for face images.normalize.m      - Normalizes an image by subtracting a                   linear lighting plane and rescaling the                   grayscale distribution histogram.augmentlr.m      - Augments an image set with the left-right                   flipped versions of the images.augmentud.m      - Augments an image set with the upside-                   down versions of the images.M-Files (Image Loading/Display)-------------------------------loadimages.m     - Loads a set of images according to the                   given pattern set.showimages.m     - Subplots a set of images in an image                   array.Image Data (Using wildcards '#')--------------------------------scaled/n##-x.PNG - Non-face files used for trainingscaled/s##-n.PNG - Face data of normal pose for s##scaled/s##-c.PNG - Face data of center lighting pose for s##scaled/s##-l.PNG - Face data of left lighting pose for s##scaled/s##-r.PNG - Face data of right lighting pose for s##scaled/s##-g.PNG - Face data of pose with glasses for s##