

![0_image_0.png](0_image_0.png) 

5 Conclusion Author has compared the performance of a simplest known method (energy density method) [17], the directional feature Method [18] and the proposed directional feature with energy method on the basis time required for training, accuracy, False Acceptance Ratio & False Rejection Ratio. After many experiments and observations author comes to the conclusion that if the samples available for training are limited then directional feature method is better then energy density but at the same time directional feature merge with the energy method shows the best of all three methods. Although time required for training is slightly greater in case of this proposed method but the accuracy and FAR are satisfactory, and that extra time can be bearable. If one has a huge database for both Genuine & Forgeries to use for training then energy density method can be used but overall performance of this proposed method is far better then previous two. It is also observed that accuracy of energy density method is increasing rapidly as training sample increases but other two methods shows almost considerable results for all the training sample sets. Author has also observed that the results for FRR are varying randomly for all the cases. It gives the reason for future studies again. 

The proposed directional feature with energy method for offline signature verification method gives far better results then the other two methods and can be implemented as easily as the previous one. 

Acknowledgments. **We are very thankful to the HOD (Electronics & Instrumentation Engineering), IET,** 
DAVV, Indore and the Director, Malwa Institute of Technology, Indore for their constant support and motivation. 

References 1. **Bai-ling Zhang, "Off-Line Signature Recognition and Verification by Kernel Principal Component SelfRegression," Fifth International Conference on Machine Learning and Applications(ICMLA'06)** 
10.1109/ICMLA.2006.37, p. 28 to 33. 

2. **Deepthi Uppalapati, "Integration of Offline and Online Signature Verification systems," Department of** 
Computer Science and Engineering, I.I.T., Kanpur, July 2007. 

3. **Rejean Plamondon and Sargur N. Srihari, "On-Line and Off-Line Handwriting Recognition: A Comprehensive**
Survey," IEEE transections on pattern analysis and machine intelligence, Vol. 12 No. 1, January 2000, pp. 63 - 
84. 

4. The IEEE website. [Online]. Available: http://www.ieee.org 5. **Department of Computer Science, IIT, Kanpur for precious imformations. Available: http://www.cse.iitk.ac.in** 
6. **H. N. Prakash and D. S. Guru, "Off-line signature verification: an approach based on score level fusion,"** 
International journal of computer application, Vol. 1-No.18, 2010. 

7. **Samaneh Ghandali and Mohsen Ebrahimi Moghaddan, "Off-line Persian Signature identification and** 
verification based on image registration and fusion," Journal of Multimedia, Vol.-4 No.3, june 2009. 