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DYNAMIC SIGNATURE VERIFICATION USING PATTERN RECOGNITION

  • Type:Project
  • Pages:40
  • Format:Microsoft Word
(Computer Science Project Topics & Materials)
ABSTRACT
In this work we describe a new approach to dynamic signature verification using the discriminative training framework. The authentic and forgery samples are represented by two separate Gaussian Mixture models and discriminative training is used to achieve optimal separation between the two models. An enrollment sample clustering and screening procedure is described which improves the robustness of the system. We also introduce a method to estimate and apply subject norms representing the "typical": variation of the subject's signatures. The subject norm functions are parameterized, and the parameters are trained as an integral part of the discriminative training. The system was evaluated using 480 authentic signature samples and 260 skilled forgery samples from 44 accounts and achieved an equal error rate of 2.25%.

TABLE OF CONTENT
TITLE PAGE
CERTIFICATION
APPROVAL
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
TABLE OF CONTENT

CHAPTER ONE
1.0 INTRODUCTION
1.1 STATEMENT OF PROBLEM
1.2 PURPOSE OF STUDY
1.3 AIMS AND OBJECTIVES
1.4 SCOPE/DELIMITATIONS
1.5 LIMITATIONS/CONSTRAINTS
1.6 DEFINITION OF TERMS

CHAPTER TWO
2.0 LITERATURE REVIEW

CHAPTER THREE
3.0 METHODS FOR FACT FINDING AND DETAILED DISCUSSIONS OF THE SYSTEM
3.1 METHODOLOGIES FOR FACT-FINDING 
3.2 DISCUSSIONS

CHAPTER FOUR
4.0 FUTURES, IMPLICATIONS AND CHALLENGES OF THE SYSTEM 
4.1 FUTURES 
4.2 IMPLICATIONS
4.3 CHALLENGES

CHAPTER FIVE
5.0 SUMMARY, RECOMMENDATIONS AND CONCLUSION
5.1 SUMMARY
5.2 RECOMMENDATION
5.3 CONCLUSION
REFERENCES

DYNAMIC SIGNATURE VERIFICATION USING PATTERN RECOGNITION

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Details

Type Project
Department Computer Science
Project ID CPU1517
Price ₦3,000 ($9)
No of Pages 40 Pages
Format Microsoft Word

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    Details

    Type Project
    Department Computer Science
    Project ID CPU1517
    Price ₦3,000 ($9)
    No of Pages 40 Pages
    Format Microsoft Word

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