fraud detection using signature recognition
TRANSCRIPT
Faculty of Engineering Technology and ResearchIsroli-Afwa, Bardoli.
Guided By:-Prof. Bhagyasri G. Patel
Fraud Detection using Signature Recognition
Prepared By:- Dhruvin L. Bhalodiya (120840131021) Akshay R. Panchal (120840131027) Santosh M. Ladani (120840131053) Tejraj G. Thakor (130843131018)
CONTENTSABSTRACT
INTRODUCTION
SYSTEM WORK-FLOW
SOFTWARE REQUIREMENT
PROJECT SCHEDULE
UML DIAGRAMS
IMPLEMENTATION
TESTING
RESULTS
APPLICATION
LIMITATION
CANCLUSION
REFERENCES
• RANDOM FORGERY• GENUINE FORGERY
Abstract The signature of person is an important biometric of a human being
which can be used to authenticate human identity. The problem arises when someone decide to imitate our signature and steal our identity.
The Image of human signature is collected by camera of mobile phone which can extract dynamic and spatial information of the signature based on Image processing techniques like Convert to gray scale, Noise Removal, Normalization, Border Elimination and Feature Extraction techniques.
The signature matching is depending on SVM. The SVM classifier is trained with sample images in database obtained from those individuals whose signatures have to be authenticated by the system. In our proposed system SQLite database as a back-end and Android platform as a front-end.
INTRODUCTION• Now days , many fraud things happens if any unknown
person wants to imitate person’s identity.
• If a person sign name of the checking account holder to check without account holder’s permission, then this is considered signature forgery.
• So signature Verification is essential in day-to - day life.
Types of Forgery1. Random forgery:
2. Blind forgery:Own style without any knowledge of spelling.
3. Skilled forgery: Experience in coping the signature.
Randomly sign. with person’s own style.
Training Signature Image
Image PreprocessingGray ScaleNoise RemovalBorder EliminationImage Normalization
Feature ExtractionParameter ExtractionGlobal ExtractionLocal Extraction
Feature ExtractionParameter ExtractionGlobal ExtractionLocal Extraction
Test Signature Image
Image PreprocessingGray ScaleNoise RemovalBorder EliminationImage Normalization
Recognition and Verification Process
Genuine or Forgery
Feature Database(SQLite)
SYSTEM WORK-FLOW
DATA ACQUISITIONCapture Signature Image from Camera.
IMAGE PROCESSING
Gray scale Conversion Image smoothing
Color Image Gray Scale Image
Average Method: (R+G+B / 3).
Noise Removal Images corrupted due to positive and negative
stemming from decoding errors or noisy channels.
Median filter
Color to Gray Scale Noise Removal Image
Border Elimination Detecting sharp changes in Image Brightness to
capture important property of images.
Vertical and Horizontal ProjectionCanny Edge Detection Algorithm
Border Eliminate image
image NormalizationSignature height and width may vary due to theirregularities in the image scanning.
Normalized Image
Linear normalization of a grayscale image is
FEATURE EXTRACTION
Features Extraction :- Similar characteristics of images that
accurately retrieve features.
Parameter Extraction
Global
Extraction
Local
Extraction
Types of Feature Extraction
1. Parameter Extraction :- I. Horizontal projectionII. Vertical projectionIII. Center of gravityIV. Height and Width
2. Global Extraction :- I. Aspect ratio II. Histogram.
3. Local Extraction:- Properties of signatures image in specific part.
1. Parameter Extraction
Vertical Projection
1.Horizontal Projection
Horizontal ProjectionOriginal Image
Original Image
2. Vertical Projection
SOFTWARE REQUIREMENT Software Requirement :-
Front End – Eclipse IDE(Android) Back End – SQLite Database
Hardware Requirement :- Android Version 2.3.0(Ginegerbread) 512 MB RAM 2 Megapixel Camera 1 GHz Processor
Technology Requirement :- Eclipse IDE OpenCV for image processing SQLite Database
Work-Flow of Project
GRAPHICAL REPRESENTATION OF WORK-FLOW PROJECT
UML DIAGRAMS
1. USE-CASE DIAGRAM
User
Login
User_Name
Delete Sign_Image
Password
Upload Sign_Image
Intiatalize
Update Sign_Image
Global Feature
Camera
Image_Processing
Local Feature
Validation
Gallery
Matching
Feature Extract
Signature Recognition
SystemConversion to Gray Scale Normalize
Tested Image Normal Image
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2. SEQUENCE DIAGRAMUser Signature Recognition
System
2: Check credential of User
3 : Login Successfully
4 : Upload Sign_Image
5:Image Processing
6 : Feature Extraction
7 : Matching
3. ACTIVITY DIAGRAM
Login
Select Image
Login Failed
Image Processing
[From]
[From]
Camera
Gallery
I) For Login:-
II) IMAGE PROCESSING AND VERIFICATION
Image
Convert to Gray Scale
Remove Noise
Normalize Image
Feature Extraction
Normal Image Tested Image
Signature Matching
Validation
If Noise
No Noise
Genuine Forger
If Match
No MatchValid Not Valid
4. E- R DIAGRAM
5. DATA FLOW DIAGRAM(DFD)
• Level 0 (Context Level) :
Level 1 :
User ApplicationCapture Image
ImageProcessing
Feature Extraction
Open
Signature Image Extracted By
Matching Signature
Validate Signature
Select Option1.0 1.1 1.3
1.41.51.6
Database
Signature Match
Check Validation
Process To
LEVEL 2 :
Image Noise from Image
Gray Scale Image
Normalization
Conversion To
Remove
Normalize Image By
Image Processing
Process2.0 2.1
2.2 2.3
2.4
Feature Extraction Local Feature
Global Feature
Extract
3.0 3.1
3.2
6. CLASS DIAGRAM
IMPLEMENTATION Capture Images
GRAY SCALE IMAGE
EDGE DETECTION IMAGE
MAIN SCREEN
MATCH SIGNATURE
RESULT OF MATCH SIGNATURE
RESULT
APPLICATIONS OF SIGNATURE RECOGNITION
1. Banking
2. Passport verification system.
3.Provides authentication to a candidates in public examination from their signatures.
LIMITATION• Signature Image stored Temporarily.
• Matching of Signature image based on dynamic Euclidian distance. So variation may be possible.
REFERENCES[1] Ashish Dhawan, Aditi R. Ganesan, “Handwritten Signature Verification”, The University of Wisconsin.
[2] Brooks, F. (1995) The Mythical Man Month, Addison-Wesley.
[3] Dr. S. Adebayo Daramola, Prof. T. Samuel Ibiyemi, “Offline Signature Using Hidden Markov Model(HMM)”, International Journal of Computer Application, Nigeria, November-2010.
[4] K.A. Vala, N. P. Joshi, “A Survey on Offline Signature Recognition and Verification Schemes”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Gujarat, India, March-2014.
[5] Madhuri Yadav, Alok Kumar, Tushar Patnaik, Bhupendra Kumar, “A Survey on Offline Signature Verification”, International Journal of Engineering and Innovative Technology (IJEIT), January-2013.
THANK YOU……………