Project Report Title Pages

FACE RECOGNITION SYSTEM

BY

GOVIND SINGH BISHT 08430101018

PRASHANT KUMAR 08430101060

DEEPAK SINGH AIR 08430101014

SAURABH MISHRA 08430101048

Department of Computer Science and EngineeringQuantum School of Technology, Roorkee

(Affiliated to Uttarakhand Technical University)

2012

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FACE RECOGNITION SYSTEM

Project report submitted in partial fulfilment of the requirement for the award of the Degree of B.Tech

By





Department of Computer Science and EngineeringQuantum School of Technology, Roorkee

(Affiliated to Uttarakhand Technical University)

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CERTIFICATE

This is to certify that the project report entitled FACE VARIFICATION being

submitted by Mr Govind Singh Bisht, Mr Prashant Kumar, Mr. Deepak Singh Bisht,

Mr. Saurabh Mishra in partial fulfillment for the award of the Degree of Bachelor of

Technology in Computer Science and Engineering to the Uttarakhand Technical

University is a record of bonafide work carried out by him/her under my guidance and

supervision.

Project Incharge Guide HOD (CSE/IT)

Mrs.Ruchika Monga Mr. Anchit Bijalwan Mr. N. S. Chetty

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DECLARATION

I hereby declare that the project entitled “FACE VERIFICATION” submitted for the

B.Tech Degree in Computer Science and Engineering / Information Technology is my

original work and the project has not formed the basis or submitted for the award of any

degree, diploma, or any other similar titles in any other college / institute / university.





Place:

Date:

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ACKNOWLEDGEMENT

We convey our most sincere heartfelt thanks to Head of the Department of Computer

Science and Engineering Mr. N S Chetty, for the motivation she had given as during the

progress of this project work.

At the outset we are very much thankful to our Project Incharg Mrs. RuchikaMonga,

for all the facilities provided to us in carrying out this project.

We convey our most sincere thanks to Mr. Anchit Bijalwan, Department of CSE, for

his guidance and effort throughout this project for us.

We also convey our heartfelt thanks to all the individuals who have helped me directly

and indirectly to carry out this project successfully.

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ABSTRACT

The proposed approach essentially was to implement and verify the algorithm Eigenfaces

for Face Recognition, which solves the recognition problem for two dimensional

representations of faces, using the principal component analysis.From Principal Component

Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the

Euclidean structure of face space. The snapshots, representing input images for the

proposed system, are projected in to a face space (feature space) which best defines the

variation for the face images training set. The face space is defined by the ‘eigenfaces’

which are the eigenvectors of the set of faces. These eigenfaces contribute in face

reconstruction of a new face image projected onto face space with a meaningful

(named ).The projection of the new image in this feature space is then compared to the

available projections of training set to identify the person using the Euclidian distance. The

implemented system is able to perform face recognition and can give feedback giving a

window with the subject's info from database.

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TABLE OF CONTENTS

1. INTRODUCTION 1

2. SOFTWARE AND HARDWARE REQUIREMENT 3

2.1 HARDWARE 3

2.2 SOFTWARE 3

3. LITERATURE STUDY 4

3.1 BIOMETRIC RECOGNITION 5

3.2 EMERGENCE 5

3.3 ADVANTAGES 5

3.4 TYPES 6

3.4.1 EYE 7

3.4.2 IRIS 7

3.4.3 RATINA 7

3.4.4 FACE 7

3.4.5 FINGER 8

3.4.6 HAND 8

3.4.7 HAND GEOMETRY 8

3.4.8 VEIN PATTERN RECOGNITION 8

3.4.9 PALM 9

3.4.10 VOICE 9

3.5 MODULES OF BIOMETRICS 9

3.5.1 SENSOR OR CAPTURE MODULE 9

3.5.2 FEATURE EXTRACTION MODULE 10

3.5.3 MATCHER MODULE 10

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3.5.4 SYSTEM DATABASE MODULES 10

3.6 TYPICAL BIOMETRIC RECOGNITION SYSTEM 11

3.6.1 FACIAL RECOGNITION 11

3.6.2 AUTOMATIC FACIAL RECOGNITION PROCESS FLOW 12

3.6.3 FACIAL RECOGNITION TECHNIQUES 14

3.6.3.1 EIGEN FACES 14

3.6.3.2 NEURAL NETWORK 14

3.6.3.4 GRAPH MATCHING 14

3.6.3.5 HIDDEN MARKOVER MODELS(HMMS) 14

3.6.3.6 GEOMATRICAL FEATURE MATCHING 15

3.6.3.7 TEMPLATE MATCHING 15

3.6.3.8 3D MORPHABLE MODEL 15

4. SOFTWARE REQUIREMENT ANALYSIS 16

4.1 PROBLEM DEFINITION 16

4.2 MODULES AND THEIR FUNCTIONALITIES 17

4.2.1 INTRODUCTION 17

4.2.2 COLOR BASED TECHNIQUES 18

4.2.3 PCA 19

4.2.3.1 FACE FORMATION PHASE 21

4.2.3.2 TRAINING PHASE 21

4.2.3.3 RECOGNITION AND LEARNING PHASE 21

4.2.3.4 THE USE OF EIGENFACES FOR RECOGNITION 21

4.2.4 THE USE OF EIGEN FACES FOR RECOGNITION 23

4.2.4.1 EIGENVALUES AND EIGEN VECTORS 23

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4.2.5 COMPUTATION STEP 25

4.2.6 CLARIFYING IMAGE 28

4.2.7 I/P AND O/P 29

5. SOFTWARE DESIGN 30

5.1 DATA FLOW DIAGRAM 30

5.1.1 LEVEL 0 31

5.1.2 LEVEL 1 32

5.1.3 PROCESSING 33

5.1.4 RECOGNITION 34

5.1.5 LEVEL 2 35

5.2 UML DIAGRAM 36

5.2.1 USECASE DIAGRAM 36

5.2.2 SEQUENCE DIAGRAM 37

5.2.3 OBJECT DIAGRAM 38

5.3 CONTROL FLOW DIAGRAM 39

5.3.1 COLOR BASED TECHNIQUES 39

5.3.2 PCA TRAINING 40

5.3.3 PCA RECOGNITION 41

6. CODING 42

7. OUTPUT SCREENS 72

8. CONCLUSION 77

9. FURTHER ENHANCEMENT AND RECOMMENDATION 78

10. REFERENCE 80

11. APPENDIX 81

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LIST OF DIAGRAMS

3.6. TYPICAL BIOMETRIC RECOGNITION SYSTEM 10

3.6.4 FACE RECOGNITION PROCESSING FLOW 12

5.1.1 LEVEL 0 DFD 31


5.1.3 DATA FLOW DIAGRAM PROCESSING 33

5.1.4 DATA FLOW DIAGRAM RECOGNITION 34


5.2.1 USE CASE DIAGRAM 35

5.2.2 SEQUENCE DIAGRAM 36

5.3 OBJECT DIAGRAM 38

5.4.1.COLOR BASED TECHNIQUE 39

5.4.2.PCA TRAINING PHASE 40

5.4.3.PCA RECOGNITION PHASE 41

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LIST OF IMAGES

4.2.4 SAMPLE EIGENFACES FORM THE SAMPLE TRAINING SET 25

7.1 A SIMPLE FORM 72

7.2. AN UNKNOWN PERSON 73

7.3. ADDING A NEW PERSON TO THE DATABASE 74

7.3. ANOTHER FACE OF SAME PERSON ADDED TO GET THE BETTER RESULTS

75

7.4. RECOGNIZING A PERSON REGISTERED IN THE DATABASE 76

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