fraud detection using signature recognition

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……………