The document discusses face recognition techniques using PCA, LDA, and a genetic algorithm. It begins with an overview of face recognition and challenges. It then describes 5 face databases used in the study. Experimental results showing the recognition accuracy of PCA and LDA on the databases are presented. A proposed method applying a genetic algorithm to face recognition is described. The genetic algorithm approach aims to address issues with PCA and LDA, such as requiring multiple images per person. Experimental results showing the improving recognition accuracy of the genetic algorithm over generations are also presented. The conclusions discuss how the genetic algorithm approach reduces problems with PCA and LDA, such as data storage and computation requirements.

1. STUDY AND ANALYSIS OF NOVEL FACE
RECOGNITION TECHNIQUES USING PCA,
LDA AND GENETIC ALGORITHM
By:
Sadique Nayeem
Pondicherry University

2. Outline
Overview
Image Database
PCA & LDA Experimental Result
Proposed Method
Implementation
Experimental Result
Conclusions
2

3. Overview
 The face plays a major role in our social interaction in conveying
identity and emotion.
 Face recognition by human is quite robust, despite large changes in
the visual stimulus due to viewing conditions, expression, aging,
and distractions such as glasses or changes in hairstyle.
 Developing a computational model of face recognition is quite
difficult, because faces are complex, multidimensional, and subject
to change over time.
 In the last two decade, a number of face recognition technique has
been developed, but they lack in robustness and they work well for
specific face databases.
3

4. Image Database
Name
of
databas
e
Source Image
format
Image
size
Imag
e
type
Number
of unique
individua
l
Total
numb
er of
image
s
Variations Sample
Image
IFD IIT
Kanpur
JPEG 110 X 75 Color 60 660 8 pose,
3 emotion
Essex
face
databas
e -
face94
University
of Essex,
UK
JPEG 90 X 100 Color 152 3040 facial
expression,
slight head
tilt.
Yale Yale
university
GIF 320 X
243
Gray 15 165 facial
expression,
w/o glasses
Face
1999
California
Institute
of
Technolo
gy
JPEG 300 X
198
Color 26 450 lighting,
expression,
Background
UMIST University JPEG 92 X 112 Gray 20 564 Vary pose
4

5. PRINCIPLE COMPONENT ANALYSIS
RESULT
5
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11
IFD
Face94
Yale
Face 1999
UMIST
Number of samples
RecognitionAccuracy(%) NUMBER OF INDIVIDUALS: 273
NUMBER OF IMAGES USED : 18018
Fig. 1 Result of PCA

6. LINEAR DISRIMINANT ANALYSIS RESULT
6
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11
IFD
Face94
Yale
Face 1999
UMIST
NUMBER OF INDIVIDUALS: 273
NUMBER OF IMAGES USED : 18018
RecognitionAccuracy(%)
Number of samples
Fig. 2 Result of LDA

7. PROPOSED METHOD
7

8. Genetic Algorithm Applied to Face
Recognition
A method for face recognition by genetic algorithm has been proposed.
First
of all, a set of training images and testing images are given
STEPS:
1. Convert all the images of the training set into gray scale then into
column vector as shown in the figure below:
8
Fig. 3 Converting training set image into column vector

9. 2. Select the image (to be tested) from the testing set, convert the
image into gray scale then into column vector as shown in the
figure below:
3. For more than one sample per person apply crossover operator to
produce more number of images per person otherwise go to step
4.
9
a b c d
0 0 0 1 0 0 1 0
0 0 0 1 1 0 0 0
I.
0 0 0 1 0 0 1 0
0 0 0 1 1 0 0 0
II.
0 0 0 1 0 0 0 0
0 0 0 1 1 0 1 0
III.
Genetic Algorithm Applied to Face
Recognition
Fig. 4 Converting testing image into column vector

10. Genetic Algorithm Applied to Face
Recognition
4. For one sample per person apply mutation at the least significant
bits of chromosome.
5. Determine the fitness function value by using the Euclidian
distance between the test image and the training set images.
10
a b
Fig. 5 Mutation applied to image vector

11. Genetic Algorithm Applied to Face
Recognition
6. If any individual obtain a value of the fitness function below the
threshold one, the system recognizes the image same as the test
image, otherwise.
7. Increase the generation count. Go to step 3 and repeat step 3 to 8
till the counter has reached a maximum number generation T
(defined by the user).
11

12. EXPERIMENTAL RESULTS OF
GENETIC ALGORITHM APPLIED
TO FACE RECOGNITION
12

13. Selection of Training Set and Testing set
13
Fig. 6 Selecting training database Fig. 7 Selecting training database

14. Selection of Test Image & Output
14
Fig. 8 Input the test image.
Fig. 9 Test image as the input Fig. 10 Equivalent image as the output

15. Result at Generation: 0
15
20
30
40
50
60
70
80
90
100
1 2 3 4 5
IFD
Face94
Yale
Face 1999
UMIST
Generation: 0
Number of samples
RecognitionAccuracy(%)
Fig. 11 Result at Generation 0

16. Result at Generation: 1
16
Generation: 1
20
30
40
50
60
70
80
90
100
1 2 3 4 5
IFD
Face94
Yale
Face 1999
UMIST
Number of samples
RecognitionAccuracy(%)
Fig. 12 Result at Generation 1

17. Result at Generation: 2
17
20
30
40
50
60
70
80
90
100
1 2 3 4 5
IFD
Face94
Yale
Face 1999
UMIST
Generation: 2
Number of samples
RecognitionAccuracy(%)
Fig. 13 Result at Generation 2

18. Result at Generation: 3
18
20
30
40
50
60
70
80
90
100
1 2 3 4 5
IFD
Face94
Yale
Face 1999
UMIST
Generation: 3
Number of samples
RecognitionAccuracy(%)
Fig. 14 Result at Generation 3

19. Result at Generation: 4
19
20
30
40
50
60
70
80
90
100
1 2 3 4 5
IFD
Face94
Yale
Face 1999
UMIST
Generation: 4
Number of samples
RecognitionAccuracy(%)
Fig. 15 Result at Generation 4

20. Conclusions
 PCA and LDA technique for face recognition fails for one image per
person but gives good result for around 10 image per person.
 Collection, storage and computation of 10 images per person for face
recognition system is not possible.
 Genetic algorithm provides good result for one image per person and
instead of 10 images per person in PCA and LDA, Genetic algorithm
gives almost same result with 5 images per person.
 Thus application of genetic algorithm reduces the problems of
collection and storage of images and computation complexity of the
face recognition system.
 In future different classifier can be used in place of PCA.
20

21. Publication
 “A Study on Face Recognition Technique based on Eigenface”, Dr.
S. Ravi, Sadique Nayeem, International Journal of Applied
Information Systems (IJAIS), Foundation of Computer Science
FCS, New York, USA Volume 5– No.4, March 2013.
 “Face Recognition using PCA and LDA: Analysis and Comparison”,
Dr. S. Ravi, Sadique Nayeem. Uploaded in “International
Conference on Advances in Recent Technologies in Communication
& Computing 2013”, to be organized by ACEEE.
21

22. Reference
1. “Eigenfaces for recognition”, M. Turk and A. Pentland, Journal of Cognitive
Neuroscience, vol.3, No.1, 1991
2. “Automatic recognition and analysis of human faces and facial expressions: A survey”,
A. Samal and P. A. Iyengar, Pattern Recognition, 25(1): 65-77, 1992
3. “Using Discriminant Eigenfeatures for Image Retrieval”, D.L.Swets and J. Weng, IEEE
Transaction on Pattern Analysis and Machine Intelligence, Vol. 18, No. 8 August 1996.
4. “The Indian Face Database”, Vidit Jain, Amitabha Mukherjee, 2002, http://vis-
www.cs.umass.edu/~vidit/IndianFaceDatabase/
5. “Essex face database -face94”, University of Essex, UK,
http://cswww.essex.ac.uk/mv/allfaces/index.html
6. “Yale Database”, http://cvc.yale.edu/projects/yalefaces/yalefaces.html
7. “FACE 1999”, http://www.vision.caltech.edu/html-files/archive.html
8. UMIST Face Database, http://www.sheffield.ac.uk/eee/research/iel/research/face
9. “Handbook of Face Recognition”, Stan Z. Li. and Anil K. Zain, Springer.
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23. Thank You !
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