Automatic Verification for Handwritten Based on GLCM Properties and Seven Moments

Authors

  • Saman M. Almufti Department of Computer Science, Nawroz University, Duhok, Kurdistan Region – Iraq
  • Bara’a Wasfi Salim ITM Dept., Technical College of Administration, Duhok Polytechnic University, Kurdistan Region – Iraq
  • Renas Rajab Asaad Department of Computer Science, Nawroz University, Duhok, KRG -Iraq

DOI:

https://doi.org/10.25007/ajnu.v12n1a1651

Keywords:

Handwritten, GLCM, Seven Moments

Abstract

In recent years, the need for community verification of personality has increased dramatically, and biometrics are becoming very important in many daily applications. Biometrics work to verify individuals based on measurable data for their descriptions and characteristics. Biometric systems have thus been able to verify or identify a person.

The fact that the use of handwritten signatures are recognized in all societies, and because they are biometric, has made signature verification an important biometric process. In this paper, a number of models are collected from hand signatures for a person to study the characteristics of the parity matrix for each of these signatures for the purpose of finding common factors between the characteristics of the matrix of these models. The practical aspect summarizes the removal of all empty spaces outside the frame of the signature image to be followed by the process of unifying the dimensions, and then drawing four characteristics of the matrix of the dialogues. The process is conducted on a sample of 100 samples to sign the person to draw the qualities of his signature and then find the common characteristics among those four attributes of the signatures group.

The tests conducted on 10 people proved that the adoption of the plastic qualities offers a clear distinction between people's manual signatures. More than 80% of the signatures of people who were approved for the test were found. Thus supporting the possibility of being adopted as a biometric standard for personal verification, Matlab has been adopted for the implementation of the software.

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References

J. Sternberg,” Automated signature verification using handwriting pressure”, WESCON Technical Papers, 1975.

R.N. Nagel and A. Rosenfeld, “Computer detection of freehand forgeries”, IEEE Trans Computers, 1977.

N. Herbst and C. Liu,” Automatic signature verification based on accelerometry”, IBM J, 1977.

R. Plamondon and S.N. Srihari,” On-line and off-line handwriting recognition, IEEE, 2000.

Asaad, Renas Rajab. (2014). An Investigation of the Neuronal Dynamics Under Noisy Rate Functions. Thesis (M.S.), Eastern Mediterranean University, Institute of Graduate Studies and Research, Dept. of Computer Engineering, Famagusta: North Cyprus.

M.I Malik, M. Liwicki, L. Alewijnse, W. Ohyama, M. Blumenstein, and B. Found.,”competitions on signature verification and writer identification for on- and offline skilled forgeries” , 12th International Conference on Document Analysis and Recognition, 2013.

F. Vargas, M.A. Ferrer, C.M. Travieso, and J.B. Alonso,”Off-line signature verification based on high pressure polardistribution”. In 11th Int. Conf. on Front. in Handwriting

Recognit, 2008.

D. Impedovo, G. Pirlo, and R. Plamondon,” Handwritten signature verification: New advancements and open”, 13th Int. Conf. on Frontiers in Handwriting Recognit., Italy, September, 2012.

S. N. Srihari, E. Cohen, J. J. Hull and L. Kuan,” A system to locate and recognize ZIP codes in hand- written addresses”, IJRE 1, 1989.

F. Kimura, K. Takashina, S. Tsuruoka and Y. Miyake, “Modified quadratic discriminant functions and the application to Chinese character recognition”, IEEE, 1987.

.F. Kimura, S. Tsuruoka and Y. Miyake, “On avoiding peaking phenomenon of the quadratic discriminant function”, 8th Int. Conf. Pattern Recognition, Paris, 1986.

Parodi, M., G´omez, J.C., Alewijnse L. and Liwicki, M.,” Online Signature Verification: Automatic Feature Selection vs. FHE’s Choice”. Journal on Forensic Document Examination, Vol. 24 (2014).

M. Parodi and J.c. Gomez.,“Emerging Aspects in Handwritten Signature Verification”, World Scientific, 2014.

Terissi, L. Sad, G., G´omez, J.C. and Parodi, M.,” Noisy Speech Recognition based on Combined Audio-Visual Classifiers”, Stockholm, Sweden,2004.

Asaad, R. R., Sulaiman, Z. A., & Abdulmajeed, S. S. (2019). Proposed System for Education Augmented Reality Self English Learning. Academic Journal of Nawroz University, 8(3), 27–32. https://doi.org/10.25007/ajnu.v8n3a366

Hall E.L., (1979): "Computer Image Processing And Recognition", Academic Press, Inc.

Abdulfattah, G. M., Ahmad, M. N., & Asaad, R. R. (2018). A reliable binarization method for offline signature system based on unique signer’s profile. INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 14(2), 573-586.

Asaad, R. R., Abdulrahman, S. M., & Hani, A. A. (2017). Advanced Encryption Standard Enhancement with Output Feedback Block Mode Operation. Academic Journal of Nawroz University, 6(3), 1–10. https://doi.org/10.25007/ajnu.v6n3a70

Zhang and A.K. Jain, “Proceeding of the International” Conference on Biometric Authentication, volume 3072 of LNCS. Springer, 2004.

Published

2023-02-14

How to Cite

M. Almufti, S., Wasfi Salim, B., & Rajab Asaad, R. (2023). Automatic Verification for Handwritten Based on GLCM Properties and Seven Moments. Academic Journal of Nawroz University, 12(1), 130–136. https://doi.org/10.25007/ajnu.v12n1a1651

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