Study and Analysis of Novel Face Recognition Techniques Using PCA, LDA and Genetic Algorithm

7/29/2019 Study and Analysis of Novel Face Recognition Techniques Using PCA, LDA and Genetic Algorithm

1/23

STUDY AND ANALYSIS OF NOVEL FACE

RECOGNITION TECHNIQUES USING PCA,

LDA AND GENETIC ALGORITHM

By:

Sadique Nayeem

Pondicherry University


2/23

Outline

Overview

Image Database

PCA & LDA Experimental Result

Proposed Method

Implementation

Experimental Result

Conclusions

2


3/23

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 forspecific face databases.

3


4/23

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 Yaleuniversity

GIF 320 X243

Gray 15 165 facialexpression,

w/o glasses

Face

1999

California

Institute

of

Technolo

gy

JPEG 300 X

198

Color 26 450 lighting,

expression,

Background

UMIST Universit JPEG 92 X 112 Gra 20 564 Var ose

4


5/23

PRINCIPLE COMPONENT ANALYSIS

RESULT5

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


6/23

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


7/23

PROPOSED METHOD

7


8/23

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 intocolumn vector as shown in the figure below:

8

Fig. 3 Converting training set image into column vector


9/23

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 d0 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.


Recognition

Fig. 4 Converting testing image into column vector


10/23


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


11/23


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 8till the counter has reached a maximum number generation T

(defined by the user).

11


12/23

EXPERIMENTAL RESULTS OF

GENETIC ALGORITHM APPLIEDTO FACE RECOGNITION

12


13/23

Selection of Training Set and Testing set

13

Fig. 6 Selecting training database Fig. 7 Selecting training database


14/23

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


15/23

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

Recognit

ionAccuracy(%)

Fig. 11 Result at Generation 0


16/23


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

Recognit

ionAccuracy(%)



17/23


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

Recognit

ionAccuracy(%)



18/23


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

Recognit

ionAccuracy(%)



19/23


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

Recognit

ionAccuracy(%)



20/23

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


21/23

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


22/23

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.

22


23/23

Thank You !

23

Study and Analysis of Novel Face Recognition Techniques Using PCA, LDA and Genetic Algorithm

Documents

Transcript of Study and Analysis of Novel Face Recognition Techniques Using PCA, LDA and Genetic Algorithm