CS539 MACHINE LEARNING. SPRING 99
Face Recognition Using Neural Networks
PROF. CAROLINA RUIZ
Department of Computer Science
Worcester Polytechnic Institute
Construct a learning system for face recognition using neural networks
and the error back propagation procedure. This project is based on the
source code and dataset provided online as a companion to Chapter 4 of
This project consists of two parts:
The following are guidelines for the training of your neural nets:
- Pose Recognizer.
Train a neural network to recognize whether the person in a picture is
looking up, straight, left, or right.
For this, you just need to follow the steps of the training process
Section 4.7 of the textbook. You should reproduce their experimental
- Sunglasses Recognizer.
Train a neural network to recognize whether or not the person in a picture is
- Code: You can use
Your code must run on the CS or CCC Unix machines.
- Training/Test Instances:
Use the "quarter"-size collection of pictures provided at the
CMU site. You can find copies of those pictures in our course
public directory /cs/courses/cs539/s99/Projects/Proj2 on the
REPORT AND DUE DATE
Project 2 is due on Tuesday, March 2 at 5:30 pm.
Your system should follow the
Departmental Documentation Standard.
- Program and Neural Networks.
You should submit (1) the source code of your program and
(2) the neural networks that you obtained, by email to
- Written Report.
Please bring your report to my office (FL232) or to class by the due
date/time. Your report should discuss the following issues:
Your report should also include a short user manual explaining how to
install, run, and use your system (if different from the CMU package).
- adaptation of the code (if any) or a description of your own code,
- the experiments you ran with the system,
- the topology (number of units in each hidden layer),
initial weights, number of iterations of the error backpropagation
algorithm, and final weights of each of your neural nets,
- evaluation of the accuracy of each of your neural nets,
- strengths and the weaknesses of your system.
- Oral Report.
We will discuss the results from the individual projects during the class
on March 2.
Be ready to show your results (I strongly encourage you to prepare
transparencies) and to discuss your project solution in class.