face recognition using pca and neural network ppt................................................................................................................................................................................................................................

1. Face Recognition
using
Neural Network
by Dong Hyun Roh
Computer Science Department
KAIST

2. content
 face recognition
 related researches
 my system
 experiments
 analysis
 future work
 references

3. face recognition
 What is face recognition?
face recognition system
face information
for each person
in a fixed domain
a person whose face is
in the input image

4. related researches
 Template matching
very simple, very sensitive to noise
 PCA(associative memory)
use PCA to extract important features and to reduce the dimension
of the input data
 Image compression(back propagation network)
use compression network to extract important features and to
reduce the dimension of the input data
this is my approach
 Others
extract visual feature like edges, shape of each components, etc.

5. My system
 Object
face recognition for the face image domain in which there are some
small variations
 face Image domain
15 persons
12 images per person
variation such as expression, direction of light, noise, glasses
cen gla hap lef nog noi
nor rig sad sle sur win

6. My system
 Procedure
Normalization
Hidden nodes
of
Compression
network
Recognition
network
Person 1

7. My system
 Normalization
original image : 320*243
normalized image : 32*24
block size : 10*10
averaging the intensity of pixels in block
 compression network
input = raw data of image
output = raw data of image ( same as input )
32*24 - 40 - 32*24 structure
backpropagation learning algorithm

8. My system
 recognition network
input = values of hidden layer of compression network
output = a person whose face is in the image
40 - 10 - 15 structure
backpropagation learning algorithm

9. Experiment 1
 Compression network
training data
nor images of 15 persons
2000 trainings, learning rate = 0.005
result(sample)
cen gla hap lef nog
noi
nor
rig sad sle sur win
Training
image

10. Experiment 2
 Recognition network
use the result of experiment 1
training data
nor images of 15 persons
2000 trainings, learning rate = 0.05
result
total face image 180 ( including the training data)
the rate of correct recognition = 133/180*100 = 73.9%
the distribution of errors
nor cen gla hap lef nog noi rig sad sle sur win
0 6 3 3 12 2 0 12 2 2 2 3

11. Experiment 3
 Recognition network
not use the result of experiment 1
assign random 40 dimensional key to each image
training data
 nor image of 15 persons
2000 trainings, learning rate = 0.05
result
total face image 180 ( including the training data)
the rate of correct recognition =12/180*100 = 6.7%
the distribution of errors
nor cen gla hap lef nog noi rig sad sle sur win
14 14 14 14 14 14 14 14 14 14 14 14

12. Experiment 4
 Recognition network
not use compression network
use raw image(32*24) for training and test
training data
nor image of 15 persons
2000 trainings, learning rate = 0.05
32*24 - 30-15 structure
result
total 180 images, the rate of correct recognition = 6.1%
the distribution of errors
nor cen gla hap lef nog noi rig sad sle sur win
14 14 14 15 14 14 13 15 14 14 14 14

13. Analysis
 The hidden of compression network
encode the inputs in a smaller dimensional subspace that retains
most of the important information
if the hidden units are linear, the best solution to this problem is the
least squares solution(i.e. to have the hidden units span the L
principle components with the highest eigenvalues)
Cottrell et al. Found that the weights of 16 hidden units span the
space of the 13 first engenvectors of he covariance matrix of the
inputs.
when transformed to gray scale and graphically displayed, the
hidden unit receptive and projective fields looked “face-like” and
showed some similarity to the eigenvectors or eigenfaces
The hidden value of compression network is very
useful in face recognition!!

14. Further work
 Improvement of the performance of face recognition
improvement of the compression network
 If the compression network is trained with all face images,
what will be different from experiment 2 ?
The performance of compression network
The performance of recognition network

15. Reference
 Dominque Valentin, Herve Abdi, Alice J. O’Toole, Garrison
W. Cottrell, “Connectionist Models of Face Processing: A
survey”, 1994