Abstract We exploited the geometrical and statistical properties of signature images for offline signature verification and identification in this paper, using signature clustering and classification based on extracted features. The Offline-SVR has been tested on the 2004 Ministerio de Ciencia Tecnología e Innovación (MCTYTDB) OffLineSignSubCorpus dataset, the MCYT-330 online signature dataset, and the MCE-200 dataset, which together are referred to as the MCE-605 dataset. Using a standard data set for experiments, the results of the Vector Distance (VD), Support Vector Machine (SVM) and Neural Network (NN) methods are significantly superior to those of other signature verification and recognition methods. Moreover, the VD method performed better than The SVM and NN methods. The purpose of the study is on clustering signature images using geometric and statistical features, as well as the utilization vector distance, neural networks, and support vector machines for signature image verification and identification. It was decided to use the algorithm for developing geometric and statistical features. The signature images are classified using generated features using k-means clustering, and Offline and Online- Support Vector Regression (SVR) is accomplished using VD, SVM, and NN training and classification with a different number of signatures each time, preceded by verification using recognition statistics. Because of the minimal number of features, the designed mechanism seems to be much faster. Experimenting on a standard dataset reveals that the results obtained from clustering signatures and categorization are effective and simple in comparison to other Offline signature confirmation systems. In this research work, we address the problem of representing handwritten signatures (online/offline) suitable for effective verification and recognition. We propose effective feature extraction for verification and recognition of signatures.

References

[1] Aravinda C., Meng L., and Reddy U., “An Approach for Signature Recognition Using Contours-Based Technique,” in Proceedings of International Conference on Advanced Mechatronic Systems, Kusatsu, pp. 46-51, 2019.

[2] Aravinda C., Meng L., Reddy U., and Prabhu A., “Signature Recognition and Verification Using Multiple Classifiers Combination of Hu’s and HOG Features,” in Proceedings of International Conference on Advanced Mechatronic Systems, Kusatsu pp. 63-68. 2019.

[3] Bansal A., Garg D., and Gupta A., “A Pattern Matching Classifier for Offline Signature Verification,” in Proceedings of 1st International 672 The International Arab Journal of Information Technology, Vol. 19, No. 4, July 2022 Conference on Emerging Trends in Engineering and Technology, Nagpur, pp. 1160-1163, 2008.

[4] Fayyaz M., Saffar M., Sabokrou M., Hoseini M., and Fathy M., “Online Signature Verification Based on Feature Representation,” in Proceedings of The International Symposium on Artificial Intelligence and Signal Processing, Mashhad, pp. 211-216, 2015.

[5] Gruber C., Gruber T., Krinninger S., and Sick B., “Online Signature Verification with Support Vector Machines Based on LCSS Kernel Functions” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 40, no. 4, pp. 1088- 1100, 2010.

[6] Guru D. and Prakash H., “Online Signature Verification and Recognition: An Approach based on Symbolic Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 6, pp. 1059- 1073, 2009

[7] Houtinezhad M. and Ghaffary H., “Improvement of Imperialist Competitive Algorithm based on the Cosine Similarity Criterion of Neighboring Objects,” The International Arab Journal of Information Technology, vol. 18, no. 3, pp. 261- 269, 2021.

[8] Iranmanesh V., Ahmad S., Adnan W., Yussof S., Arigbabu O., and Malallah F., “Online Handwritten Signature Verification Using Neural Network Classifier Based on Principal Component Analysis,” The Scientific World, pp. 357-360, 2011.

[9] Julita A., Fauziyah S., Azlina O., Mardiana B., Hazura H., and Zahariah A., “Online Signature Verification System,” in Proceedings of 5th International Colloquium on Signal Processing and its Applications, Kuala Lumpur, pp. 8-12, 2009.

[10] Justino E., Bortolozzi F., and Sabourin R., “Off- Line Signature Verification Using HMM for Random, Simple and Skilled Forgeries,” in Proceedings of 6th International Conference on Document Analysis and Recognition, Seattle, pp. 1031-1034, 2001.

[11] Kumar A. and Dhandapani S., “A Bank Cheque Signature Verification System using FFBP Neural Network Architecture and Feature Extraction based on GLCM,” International Journal of Emerging Trends\and Technology in Computer Science, vol. 3, no. 3, pp. 46-52, 2014.

[12] Kanungo T., Mount D., Netanyahu N., Piatko C., Silverman R., and Wu A., “An Efficient K- Means Clustering Algorithm: Analysis and Implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881-892, 2002.

[13] Li N., Liu J., Li Q., Luo X., and Duan J., “Online Signature Verification Based on Biometric Features,” in Proceedings of 49th Hawaii International Conference on System Sciences, Koloa, pp. 5527-5534, 2016.

[14] Liu Y., Yang Z., and Yang L., “Online Signature Verification Based on DCT and Sparse Representation,” IEEE Transactions on Cybernetics, vol. 45, no. 11, pp. 1-14, 2014.

[15] Padmajadevi G. and Aprameya K., “Comparison of Offline Signature Verification Systems with Support Vector Machines and Neural Network Classifiers Using Geometrical,” International Journal of Innovative Research in Science, Engineering and Technology, vol. 6. no. 8, pp. 16181-16190, 2017.

[16] Pushpalatha K., Prajwal S., Gautam A., and Kumar K., “Offline Signature Verification Based on Contourlet Transform and Textural Features Using HMM,” in Proceedings of International Conference on Recent Advances and Innovations in Engineering, Jaipur, pp. 1-6, 2014.

[17] Sae-Bae N. and Memon N., “Online Signature Verification on Mobile Devices,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 6, pp. 933-947, 2014.

[18] Sae-Bae N. and Memon N., “A Simple and Effective Method for Online Signature Verification,” in Proceedings of International Conference of the BIOSIG Special Interest Group, Darmstadt, pp. 1-12, 2013.

[19] Sarfraz M. and Rizvi S., “An intelligent system for Online Signature Verification,” in Proceedings of 2nd International Conference on Information Security and Cyber Forensics, Cape Town, pp. 1722, 2015.

[20] Tolosana R., Rodriguez R., et. al., “ICDAR 2021 Competition on On-Line Signature Verification,” in Proceedings of International Conference on Document Analysis and Recognition, pp. 723- 737, 2021.

[21] Venkataramu A., Masahiko A., Akshaya A., Gurupura A., and Rajashekar U., “Offline Signature Recognition and Verification Using ORBKey Point Matching Techniques,” Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 1-7, 2020.

[22] Wilkinson T., Pender D., and Goodman J., “Use of Synthetic Discriminant Functions for Hand- Written Signature Verification,” Applied Optics, vol. 30, no. 23, pp. 3345-3353, 1991. Off-Line Signature Confirmation based on Cluster Representations ... 673 Aravinda Chikmagalur Ventakaramu is currently has an Associate Professor in the Department of Computer Science and Engineering at N.M.A.M Institute of Technology Nitte, Karkala. He received his bachelor’s and master’s degree in Computer Science from Mysore University and a Ph.D. degree from Visvesvaraya Technological University Belagavi. He has received his Research Fellowship from Ritsumeikan University Japan. He has authored several peer-reviewed national and international conferences Journal papers. Suresha Devaraj currently working as Professor at Department of Information Science and Engineering at A.J Institute of Engineering and Technology, Mangaluru. He received his Bachelor of Engineering degree from Kuvempu University, Shankaraghatta, MTech and Ph.D. degree from Visveswaraya Technological University, Belagavi, Karnataka, India. He has published various Research Papers at reputed Journals/International Conference. He is reviewer of few Journals and International Conferences. Prakash Hebbakavadi Nanjundaiah currently working has a Professor in the Department of Computer Science and Engineering at Rajeev Institute of Technology, Hassan, India. He received his B.E. degree in Electronics and Communication Engineering from the University of Mysore, Mysore, India, in 1990 and the MTech degree in Electronic Instrumentation from Regional Engineering College, Warangal, India in 1995 respectively. He received the Ph.D. degree in Computer Science and Engineering from the University of Mysore, Mysore, India, in 2010. He has authored several peer reviewed papers at national and international conferences and Journals Including IEEE Transactions. His research interest includes signature analysis and retrieval, clustering, biometrics, Image processing, Pattern recognition and symbolic data analysis. Dr. Prakash is a life member of Indian professional bodies such as Institute of Engineers, Indian Society for Technical Education (ISTE) and System Society of India (SSI) and Member of Indian Institute of Engineers, India. Kyasambally Rajasekhar Udayakumar Reddy currently working has Vice-Principal and Professor, Dept of ISE at Dayanand Sagar College of Engineering and Management Bangalore. He served has Professor and Head in Computer Science and Engineering, NMAM Institute of Technology, Nitte, from May 2016 to July 2021. Professor of Computer Science and Engineering, NMAM Institute of Technology, Nitte, from August 2015 to April 2016.Professor in Computer Science and Engineering, BNM Institute of Technology, Bangalore, from August 2010 to July 2015.Assistant Professor in Information Science and Engineering, BNM Institute of Technology, Bangalore, from February 2004 to June 2007.