INFORMATION CHANGE THE WORLD

International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.9, No.7, Jul. 2017

A Clustering-based Offline Signature Verification System for Managing Lecture Attendance

Full Text (PDF, 638KB), PP.51-60


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Author(s)

Laruba Adama, Hamza O. Salami

Index Terms

Clustering;offline;signature;verification;attendance management

Abstract

Attendance management in the classroom is important because in many educational institutions, sufficient number of class attendance is a requirement for earning a regular grade in a course. Automatic signature verification is an active research area from both scientific and commercial points of view as signatures are the most legally and socially acceptable means of identification and authorization of an individual. Different approaches have been developed to achieve accurate verification of signatures. This paper proposes a novel automatic lecture attendance verification system based on unsupervised learning. Here, lecture attendance verification is addressed as an offline signature verification problem since signatures are recorded offline on lecture attendance sheets. The system involved three major phases: preprocessing, feature extraction and verification phases. In the feature extraction phase, a novel set of features based on distribution of black pixels along columns of signatures images is also proposed. A mean square error of 0.96 was achieved when the system was used to predict the number of times students attended lectures for a given course.

Cite This Paper

Laruba Adama, Hamza O. Salami,"A Clustering-based Offline Signature Verification System for Managing Lecture Attendance", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.7, pp.51-60, 2017. DOI: 10.5815/ijitcs.2017.07.06

Reference

[1]S. Kadry, and M. Smaili, “Wireless attendance management system based on iris recognition,” Scientific Research and Essays, vol. 5, no. 12, pp. 1428-1435, 2013.

[2]N. K. Balcoh, M. H. Yousaf, W. Ahmad, and M. I. Baig, “Algorithm for efficient attendance management: Face recognition based approach,” International Journal of Computer Science Issues, vol. 9, no. 4, pp. 146-150, 2012.

[3]Z. Singhal, and R. K. Gujral, “Anytime anywhere-remote monitoring of attendance system based on RFID using GSM network,” International Journal of Computer Applications, vol. 39, no. 3, pp. 37-41, 2012.

[4]O. T. Arulogun, A. Olatunbosun, O. A. Fakolujo, and O. M. Olaniyi, “RFID-based students attendance management system,” International Journal of Scientific & Engineering Research, vol. 4, no. 2, pp. 1-9, 2013.

[5]K. Huang, and H. Yan, “Off-line signature verification based on geometric feature extraction and neural network classification,” Pattern Recognition, vol. 30, no. 1, pp. 9–17, 1997.

[6]M.S. Arya, and V.S. Inamdar, “A Preliminary Study on Various Off-line Hand Written Signature Verification Approaches,” International Journal of Computer Application, IJCA, vol. 1, no. 9, pp. 50–56, 2010.

[7]D. Impedovo, and G. Pirlo, “Automatic Signature Verification: The State of the Art,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) IEEE Trans. Syst., Man, Cybern. C, vol. 38, no. 5, pp. 609–635, 2008.

[8]J. Coetzer, and J. Dupreez, “Off-line signature verification: a comparison between human and machine performance,” In: Tenth International Workshop on Frontiers in Handwriting Recognition. Suvisoft, 2006, pp.481-485.

[9]F. Leclerc, and R. Plamondon, “Automatic Signature Verification: The State Of The Art-1989-1993,” Series in Machine Perception and Artificial Intelligence Progress in Automatic Signature Verification, 1994, pp. 3–20.

[10]R. Kumar, J.D. Sharma, and B. Chanda, “Writer-independent off-line signature verification using surroundedness feature,” Pattern Recognition Letters, vol. 33, no. 3, pp. 301–308, 2012.

[11]K. Tsiptsis, and A. Chorianopoulos, Data Mining Techniques in CRM: inside customer segmentation, John Wiley & Sons, 2011.

[12]I. H. Witten, and E. Frank, Data mining: practical machine learning tools and techniques, Amsterdam: Morgan Kaufmann, 2005.

[13]J. Han and M. Kamber, “Data mining: concepts and techniques,” Amsterdam: Elsevier/Morgan Kaufmann, 2011.

[14]L. Rokach, and O. Maimon, “Clustering Methods,” Data Mining and Knowledge Discovery Handbook, 2005, pp. 321–352.

[15]S. Chen, and S.  Srihari, “Use of exterior contours and shape features in off-line signature verification,” In: Eighth International Conference on Document Analysis and Recognition. ICDAR, 2005, pp. 1280-1284.

[16]S. A. Daramola, and T. S. Ibiyemi, “Novel feature extraction technique for off-line signature verification system,” International Journal of Engineering Science and Technology, vol. 2, no. 7, pp. 3137-3143, 2010.

[17]M. K. Kalera, S. Sriharl, and A. Xu, “Offline Signature Verification And Identification Using Distance Statistics,” International Journal of Pattern Recognition and Artificial Intelligence. IJPRAI, vol. 18, no. 7, pp. 1339–1360. 2004.

[18]S. Al-ma'adeed, A. Al-kurbi, A. Al-muslihl, R. Al-qahtani, and H. Kubisi, “Writer identification of Arabic handwriting documents using grapheme features,” International Conference on Computer Systems and Applications, 2008, pp. 923-924. 

[19]S. Al-ma'adeed, E. Mohammed, D. Kassis and F. Al-muslih, “Writer identification using edge-based directional probability distribution features for Arabic words,” International Conference on Computer Systems and Applications, 2008, pp. 582-590. 

[20]R. K. Bharathi, and B. H. Shekar, “Off-line signature verification based on chain code histogram and Support Vector Machine,” International Conference on Advances in Computing, Communications and Informatics, ICACCI, 2013, pp. 2063-2068.

[21]F. Bouchareb, R. Hamdi, and M. Bedda, “Handwritten Arabic character recognition based on SVM Classifier,” In: Third International Conference on Information and Communication Technologies: from Theory to Applications, ICTTA, 2008. pp. 1-4.

[22]C. Kruthi, and D. C. Shet, “Offline Signature Verification Using Support Vector Machine,” In: Fifth International Conference on Signal and Image Processing, 2014, pp. 3-8.

[23]J. Swanepoel, and J. Coetzer, “Feature Weighted Support Vector Machines for Writer-Independent On-Line Signature Verification,” In: fourteenth International Conference on Frontiers in Handwriting Recognition. ICFHR, 2014, pp. 434-439.

[24]J. F. Vargas, M. A. Ferrer, C. M. Travieso, and J.B. Alonso, “Off-line signature verification based on grey level information using texture features,” Pattern Recognition. PATCOG, vol. 44, no. 2, pp. 375–385, 2011.

[25]A. Choudhary, R. Rishi, and S. Ahlawat, “Off-line Handwritten Character Recognition Using Features Extracted from Binarization Technique,” AASRI Procedia, vol. 4, pp. 306–312, 2013.

[26]S. Odeh, and M. Khalil, “Apply Multi-Layer Perceptrons Neural Network for Off-line signature verification and recognition,” International Journal of Computer Science Issues. IJCSI, vol. 8, no. 6, pp. 261-266, 2011.

[27]B. H. Shekar, and R. K. Bharathi, “Off-Line Signature Verification Based on Principal Component Analysis and Multi-Layer Perceptrons,” Recent Advances in Intelligent Informatics, Springer International Publishing, pp. 101–109, 2014.

[28]O. Hilton, “The Detection of Forgery,” Journal of Criminal Law and Criminology, vol. 30, no. 4, pp. 568-599, 1939.

[29]P. Tan, M. Steinbach, and V. Kumar, Introduction to data mining, Boston: Pearson Addison Wesley, 2005, pp. 532-568.

[30]A. Shah, M. N. A. Khan, and A. Shah, “An Appraisal of Off-line Signature Verification Techniques,”, IJMECS, vol. 7, no. 4, pp. 67-75, doi: 10.5815/ijmecs.2015.04.08, 2015.