Neural Networks for Face Recognition
Companion to Chapter 4 of the textbook Machine Learning.
A neural network learning algorithm called Backpropagation is among
the most effective approaches to machine learning when the data
includes complex sensory input such as images. This web page provides
an implementation of the Backpropagation algorithm described in
Chapter 4 of the textbook Machine Learning.
It also includes the dataset discussed in Section 4.7 of the book,
containing over 600 face images.
Documentation
This documentation is in the form of a homework assignment (available
in
postscript
or latex ) that provides a step-by-step introduction to
the code and data, and simple instructions on how to run it.
Code
The
code directory contains the source code for the neural network
Backpropagation algorithm described in Chapter 4. (thanks to Jeff
Shufelt for the initial implementation.)
Data
The
face images directory contains the face image data described in
Chapter 4 of the textbook. It is stored in PGM format. You can
download a
compressed tar file of all the images (only ~10.5MB!), or a compressed
tar file of only the one-quarter size images (~0.5MB - most
students used only these images in order to save computation time).
See the documentation above for a full description of the images.
The
trainset directory contains the specifications of training and
test sets referred to in the documentation above.
Visitors from outside CMU are invited to use this material free of
charge for any educational purpose, provided attribution is given in
any lectures or publications that make use of this material.
(
another nice source of face images and code)