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eEye -Automatic Fingerprint Recognition and structural face matching on large databases

eEyeAutomatic Fingerprint Recognition and structural face matching on large databases

Software Design DocumentVersion 1.0

Department of Computer Science & Engineering

University of Moratuwa

Copyright 2008

Project Supervisor:

Dr. Chathura de Silva

BSc Eng. (Moratuwa), MEng (NTU-

Singapore), PhD (NUS)

Co-Supervisor:

Mr. Prasad Samarakoon

BSc Eng. (Moratuwa)

Project members:

K.D.V.M. Edirisinghe : 050101F

J.K.D.D. Radika : 050347M

D.N. Nakandalage : 050288G

W.P.L.C. Perera : 050331J


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Table of Content

1. INTRODUCTION ......................... .......................... .......................... ......................... ......................... 41.1 Purpose.......................... .......................... .......................... ......................... .......................... ... 4

1.2 Prerequisites ........................... .......................... ......................... .......................... .................... 4

1.3 Intended Audience ........................... ......................... .......................... .......................... ........... 4

1.4 Overview of the Document .......................... .......................... ......................... ......................... 4

2. SYSTEM OVERVIEW ......................... .......................... ......................... .......................... .................... 5

3. HIGH LEVEL ARCHITECTURAL DESIGN ........................ .......................... ......................... .................... 6

3.1 High level System Description diagram ........................... ......................... .......................... ....... 6

2.1 Data Capturing and Signal Processing Module ........................ .......................... ........................ 6

2.2 Matching Module ........................ .......................... .......................... ......................... ................ 7

2.3 Decision Making Module .......................... .......................... .......................... ......................... ... 7

2.4 Administration Module ........................ ......................... .......................... .......................... ....... 7

2.5 Data Storage Module ........................... ......................... .......................... .......................... ....... 8

4. DETAILED SOFTWARE DESIGN .......................... .......................... .......................... ......................... ... 9

4.1 Use case view .......................... .......................... ......................... .......................... .................... 9

4.1.1 Use case diagram for the Administrator .......................... .......................... ........................ 94.1.2 Use case diagram for the User ....................... .......................... ......................... .............. 10

4.2 Activity Diagrams ........................ .......................... .......................... ......................... .............. 11

1. 4.2.1 Activity diagram for Data capturing and signal processing module ........................ ......... 11

4.2.2 Activity diagram for Matching and Decision Making Modules ........................ ................. 12

4.3 Sequence Diagrams .......................... ......................... .......................... .......................... ......... 13

4.3.2 Sequence diagram for data capturing and signal processing modules ........................ ..... 13

4.3.1 Sequence diagram for Matching and Decision Making Modules......................... ............. 14

4.4 Data Flow Diagrams ......................... ......................... .......................... .......................... ......... 15

4.4.1 Dataflow diagram for person verification ........................ .......................... ...................... 15

4.4.2 Data flow diagram for person identification ......................... ......................... .................. 16

4.5 Class Diagram .......................... .......................... ......................... .......................... .................. 17

5. CONCLUSION ......................... .......................... .......................... ......................... .......................... . 20


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List of Figures

Figure 2-1 : Internal functionality of the system ....................................................................................... 5

Figure 3-1 : Overall system description diagram ...................................................................................... 6

Figure 4-1 : User case diagram for Administrator ..................................................................................... 9

Figure 4-2 : User case diagram for normal users of the system .............................................................. 10

Figure 4-3 : Activity diagram for Data capturing and signal processing module ...................................... 11

Figure 4-4 : Activity diagram for Matching and Decision Making Modules ........................ ...................... 12

Figure 4-5 : Sequence diagram for data capturing and signal processing modules ......................... ......... 13

Figure 4-6 : Sequence diagram for Matching and Decision Making Modules .......................................... 14

Figure 4-7 : Dataflow diagram for person verification ............................................................................ 15

Figure 4-8 : Data flow diagram for person identification ........................................................................ 16

Figure 4-9 : Person related class diagram ............................................................................................... 17

Figure 4-10 : Image related class diagram .............................................................................................. 18

Figure 4-11 : Template related class diagram ......................................................................................... 19


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1. INTRODUCTION

1.1 Purpose

This document provides the general design approach of the eEye project (Fingerprint and face

recognition system on large databases) including the functionalities and matters related to the

overall system. It basically focuses on specifying a high level view of the architecture of the

system and interaction among the users and the components. As eEye project is mainly based

on researches some contents of this document is expected to evolve and change throughout

the process. This document will also serve as a tool for verification and validation of the final

product.

1.2

Prerequisites

Prerequisites for this document are the Project Proposal and the Software RequirementsSpecifications document of the eEye project which were submitted to the department of

Computer Science and Engineering, University of Moratuwa.

1.3

Intended Audience

The target audience of this document consists of the project supervisors, the course

coordinator and the project team members. This Design document would give an overview

design approach for the developer.

1.4 Overview of the Document

Section 2 here deals with the overview of the system and system modules and its

functionalities. Section 3 gives the high level architecture of the system with the relationship

between various system components and provides a brief description of each module. Section

4 gives the detailed software design of the project with Use case diagrams, Activity diagrams,

Sequence diagrams and Class diagrams which are relevant to each module and also to overall

system.


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2.

SYSTEM OVERVIEW

eEye is developed with the intension of providing a more accurate system for recognizing

fingerprints and facial images. It is a researched based project which aimed to identify the faults

of currently using algorithms for fingerprint recognition and face recognition and to develop

new algorithms for matching the person's fingerprints and face images against a large database

containing over several millions of data at a high speed with a low error rate.

Our ultimate goal of the project is to study the existing algorithms, their speed and accuracy

when a large database is present and come up with a new algorithm that is best suite for the

above scenario. Based on the algorithm we come up, the system may get slight changes from

the descriptions in the below sections.

The following figure describes the main functionalities of the system.

Figure 2-1 : Internal functionality of the system

Following are the main modules of the identified system and that will described in the next

section.

(1)Data Capturing and Signal Processing Module

(2)Matching Module

(3)Decision Making Module

(4)

Administration Module(5)Data Storage Module


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3.

HIGH LEVEL ARCHITECTURAL DESIGN

3.1 High level System Description diagram

Figure 3-1 : Overall system description diagram

Overall system description diagram is described in the above diagram and main modules of the system

are described in detail below.

2.1 Data Capturing and Signal Processing Module

The data capturing module collects an image or signal of a subjects biometric characteristics

that they have presented to the biometric sensor, and outputs this image/signal as a biometric

sample.

The signal processing subsystem extracts the distinguishing features from a biometric sample.This may involve locating the signal of the subjects biometric characteristics within the

received sample (a process known as segmentation), feature extraction, and quality control to

ensure that the extracted features are likely to be distinguishing and repeatable. If quality

control rejects the received sample/s, control may return to the data capture subsystem to

collect a further sample(s).

Data capturing

Module

Signal ProcessingModule

Quality Control

Matching

Module

Decision making

module

Data storageFeature Extraction

Template creation

PresentationMatching

EnrolmentSegmentation

Sensor

Verification

/Identification

Verification/Identification

outcome

Biometriccharacteristics

Template

Enrollment

Verification/Identification


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In the case of enrolment, the signal processing subsystem creates a template from the

extracted biometric features. This biometric template is stored to reuse in the matching

subsystem.

2.2 Matching Module

In the matching module, the features are compared against one or more templates and

similarity scores are passed to the decision subsystem. The similarity scores indicate the degree

of fit between the features and template(s) compared. In some cases, the features may take

the same form as the stored template.

There are two matching approaches provided with the system called verification and

identification. In verification, one or more samples of fingerprints and facial image templates

are matched against a reference template to check whether essential features are there. In

identification, sample templates are matched against a set of templates stored in the database.

2.3 Decision Making Module

The decision subsystem uses the comparison scores generated from one or more attempts to

provide the decision outcome for a verification or identification transaction.

In the case of verification, the sample templates are matched against reference template. So

the decision should be either accept with the reference template or not. In the case of

identification, the sample templates are matched against a set of templates stored in the

database. The final result may be empty or contain only one identifier that is best fitted to the

captured image. The decision policy may allow or require multiple attempts before making an

identification decision.

2.4 Administration Module

The administration subsystem governs the overall policy, implementation and usage of the

biometric system, in accordance with the relevant legal, jurisdictional and societal constraints

and requirements. This includes:

requesting additional information from the subject

set threshold values

control the operational environment and non-biometric data storage

provide appropriate safeguards for end-user privacy

Interact with the application that utilizes the biometric system.


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2.5 Data Storage Module

This is where the user information is stored, such as the faces and fingerprints currently

recognized by the system and the personal information associated with those and also

references which are identified in the enrollment process. Each reference is associated with

details of the enrolled subject. Before storing fingerprint or face information in the enrolment

database, references may be reformatted into some biometric data interchange format

together with some metadata.


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4.

DETAILED SOFTWARE DESIGN

4.1 Use case view

4.1.1

Use case diagram for the Administrator

SYSTEM

Administrator

Read Image Check Quality &

enhance image

Extract features

and save template

Enroll user

Add user details

Add fingerprint

Image

Add face image

Check Quality &

enhance image

Read Image

Detect Minutiae

points and save template

Figure 4-1 : User case diagram for Administrator


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4.1.2

Use case diagram for the User

Verify Fingerprint

image

Verify face image

Extract Features

and match

User

Read Image

Check Quality &

enhance image

Read Image

Check Quality &

enhance image

Detect Minutiae

points and match

SYSTEM

Figure 4-2 : User case diagram for normal users of the system


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4.2

Activity Diagrams

1. 4.2.1 Activity diagram for Data capturing and signal processing module

Capture image

Sample Image

Quality Checking

Enhanece quality

Segmentation

-[Low quality]

Feature Extraction

[ Ok]

Template creation

Figure 4-3 : Activity diagram for Data capturing and signal processing module

Above activity diagram shows the sequence of activities throughout the data capturing and

signal processing modules. Input image is passed to the Data capturing Subsystem for reading

image, quality control and feature extraction. Out of signal processing module is a template

created using those extracted features.


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4.2.2 Activity diagram for Matching and Decision Making Modules

Feature extraction

Retrieve template(s) from data storageRetrieve image through sensor

Matching features

Decision making

Verification/Identification outcome

Figure 4-4 : Activity diagram for Matching and Decision Making Modules

Above activity diagram shows the sequence of activities in the matching and decision makingmodules. Matching subsystem returns the set of matching data to the decision making

subsystem and get the correct matching entry which is stored in the database.


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4.3

Sequence Diagrams

4.3.2 Sequence diagram for data capturing and signal processing modules

Figure 4-5 : Sequence diagram for data capturing and signal processing modules

Above sequence diagram shows the sequence of activities during initial image storing process.

Input image is passed to the Data capturing Subsystem for reading image, quality validation and

feature extraction. Then the captured data and the original image send to the data storing

subsystem to store in the database.


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4.3.1 Sequence diagram for Matching and Decision Making Modules

Figure 4-6 : Sequence diagram for Matching and Decision Making Modules

Sequence of activities when requests image matching process is shown in the above diagram.

Captured image follows the same process described in the storing process up to data capturing

process. After that data capturing subsystem make request to matching subsystem to compare

the input with the stored data. Matching subsystem returns the set of matching data to the

decision making subsystem and get the correct matching entry which is stored in the database.


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4.4

Data Flow Diagrams

4.4.1 Dataflow diagram for person verification

Figure 4-7 : Dataflow diagram for person verification

Comparison score

Matching

Feature reference datafrom algorithm A

Reference image stored in

the database

Feature extraction

with algorithm A

Actual Verification image

captured through sensors

Feature verification data

from algorithm A

Feature extraction

with algorithm A

Claim on the basis

of decision policy

Verification Result

(Match/not match)


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4.4.2

Data flow diagram for person identification

Figure 4-8 : Data flow diagram for person identification

Comparison scores

Matching

Feature reference datafrom data storage

Actual Verification image

captured through sensors

Feature verification datafrom algorithm A

Feature extraction

with algorithm A

Identification Result

(Best fit candidate/not

matched

Identify the best

fit among

comparison scores


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4.5 Class Diagram

+Get_id() : int

+Set_id()

+Get_name() : String+Set_name()

+Get_NIC() : int

+Set_NIC()

+Get_Address() : String

+Set_Address()

+Get_Contact_No() : int

+Set_Contact_no()

+Save() : int

-person_id : int

-name : String

-NIC_no : String

-Address : String

-Contact_no : int

-finger : Finger

-face : Face

Person

+Save() : int

+Match_finger(in finger : FingerTemplate) : Object

-finger_id : int

-image_id : int

-features : Object

FingerTemplate

+Save() : int

+match_face(in face : FaceTemplate) : Object

-face_id : int

-image_id : int

-feature : Object

FaceTemplate

1*

1*

Figure 4-9 : Person related class diagram

Person class is concern about the system users. Person class has one to many associations with"FingerTemplate" and "FaceTemplate" classes which are used to handle face and finger details

of a particular user. One user may have one or several face and finger details for identification

purposes.


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+CheckQuality() : Image

+EnhanceQuality() : Image

-SetContrast()

-SetBrightness()

+GetType() : String

+SetType()

+GetSize() : Object

+SetSize()+GetContrast()

+SetContrast()

+GetBrightness()

+SetBrightness()

-type : String-size : Object

-Contrast

-Brightness

Image

-Edgedetect()+FeatureExtraction() : Face

FaceImage

+DetectPoints()+ExtractFeatures() : Finger

fingerImage

Figure 4-10 : Image related class diagram

FaceImageand FingerImage classes are used to represent finger and face images. These classes

are inherited from a super class called Imageclass. Each Image will have several fields to hold

its properties such as type, size contrast etc. Main purposes of these classes are handling

images by getting image properties, changing qualities and extracting features.


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+Save() : int

+Match_finger(in finger : FingerTemplate) : Object

-finger_id : int-image_id : int

-features : Object

FingerTemplate

+Save() : int

+match_face(in face : FaceTemplate) : Object

-face_id : int

-image_id : int

-feature : Object

FaceTemplate

+Save() : int

-Tmp_id : int

Template

Figure 4-11 : Template related class diagram

FingerTemplateand FaceTempalteclasses are used to represent a template of finger and face

image extracted features, that is going to uses in matching and decision making modules. Theseclasses are inherited from a super class called a Template class.


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5.

CONCLUSION

eEye is a research project on creating fast and efficient algorithm for fingerprint recognition and

structural face matching on large databases which contains millions of entries. Other than that, the

project give focus on finding most effective bio recognition features in face and fingerprint which are

valuable as informatics details since. Reason for that would be the different significance in bio

recognition features among the geographical regions. Although the main purpose of this project will be

finding algorithm which satisfy the project objectives rather than the user interface and a recognition

system, this document simply provides the design details of the final end product.

design srs of the great

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