Triangular Geometric Feature for Offline Signature Verification
Handwritten signature is accepted widely as a biometric characteristic for personal authentication. The use of appropriate features plays an important role in determining accuracy of signature verification; therefore, this paper presents a feature based on the geometrical concept. To achieve the aim, triangle attributes are exploited to design a new feature since the triangle possesses orientation, angle and transformation that would improve accuracy. The proposed feature uses triangulation geometric set comprising of sides, angles and perimeter of a triangle which is derived from the center of gravity of a signature image. For classification purpose, Euclidean classifier along with Voting-based classifier is used to verify the tendency of forgery signature. This classification process is experimented using triangular geometric feature and selected global features. Based on an experiment that was validated using Grupo de Senales 960 (GPDS-960) signature database, the proposed triangular geometric feature achieves a lower Average Error Rates (AER) value with a percentage of 34% as compared to 43% of the selected global feature. As a conclusion, the proposed triangular geometric feature proves to be a more reliable feature for accurate signature verification.
 Khan, S. and Dhole, A. (2014). A Review on Offline Signature Recognition and Verification Techniques. International Journal of Advanced Research in Computer and Communication Engineering. 3(6), 79-82
 Impedovo, D., Pirlo, G., and Plamondon, R. (2012). Handwritten Signature Verification: New Advancements and Open Issues. International Conference on Frontiers in Handwritting Recognition. 18-20 September. Bari, Italy: IEEE, 367-372.
 Madhavi, M., Yaram, M. R., and Krishnaiah, R. V. (2012). Effective Implementation Techniques in Offline Signature Verification. IOSR Journal of Computer Engineering (IOSRJCE). 5(4), 25-30.
 Jain, C., Singh, P., and Chugh, A. (2014). An Offline Signature Verification System: An Approach based on Intensity Profile. International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS). 8(2), 143-146.
 Nguyen, V., Blumenstein, M., and Leedham, G. (2009). Global Features for the Off-Line Signature Verification Problem. 10th International Conference on Document Analysis and Recognition (ICDAR 2009). 26-29 July. Barcelona, Spain, 1300-1304.
 Perez-Hernandez, A., Sanchez, A., and Velez, J. F. (2004). Simplified Stroke-based Approach for Offline Signature Recognition. Proceedings of the 2nd COST Workshop on Biometrics on the Internet: Fundamentals, Advances and Applications. 25-26 March. Vigo, Spain, 89-94.
 Roy, S. and Maheshkar, S. (2014). Offline Signature Verification using Grid based and Centroid based Approach. International Journal of Computer Applications. 86(8), 35-39.
 Arathi, M. and Govardhan, A. (2014). An Efficient Offline Signature Verification System. International Journal of Machine Learning and Computing. 4(6), 533-537.
 Davda, H. V. and Gonsai, S. K. (2014). Offline Signature Verification System using Energy on Grid Level. International Journal of Engineering Research. 3(2), 104-107.
 Prakash, H. N. and Guru, D. S. (2009). Geometric Centroids and Their Distances for Off-Line Signature Verification. 10th International Conference on Document Analysis and Recognition. 26-29 July. Barcelona, Spain, 121-125.
 Prashanth, C. R. et al. (2012). DWT based Off-Line Signature Verification using Angular Features. International Journal of Computer Applications. 52(15), 40-48.
 Khachaturyan, V. (2013). An Off-Line Signature Verification. Journal of Computer Science and Applications. 1(2), 23-26.
 Samuel, D. and Samuel, I. (2010). Novel Feature Extraction Technique for Off-Line Signature Verification System. International Journal of Engineering, Science and Technology. 2(7), 3137-3143.
 Daramola, S. A. et al. (2014). Vertical Off-Line Signature Feature Block for Verification. Proceedings of the 8th WSEAS International Conference on Circuits, Systems, Signal and Communications (CCST ‘14). 10-12 January. Tenerife, Spain, 203-208.
 Zuraidasahana, Mohd Shafry and Nur Zuraifah. (2015). Feature Selection Method for Offline Signature Verification. Jurnal Teknologi. 75(4), 79-84.