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8/9/2019 Design SRS of the Great
1/20
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.