The week's pick
Random Articles
Reseach Article
Offline Signature Verification based on Geometric Features using Filter Method
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 45 |
| Year of Publication: 2023 |
| Authors: Sunil Kumar D.S. |
10.5120/ijca2023922546
|
Sunil Kumar D.S. . Offline Signature Verification based on Geometric Features using Filter Method. International Journal of Computer Applications. 184, 45 ( Feb 2023), 17-23. DOI=10.5120/ijca2023922546
Abstract
This paper presents an offline signature verification using Geometric features. In this approach the acquired signatures samples undergoes pre-processing operation which includes resize, filtering, cropping, and thinning. Then Geometric features are extracted from each signature image. The extracted features are from normalized signature area. Experiments are conducted on publically available benchmark datasets namely CEDAR and GPDS. The best feature subsets of the data sets were selected using filter and wrapper methods. Based on the feature vector, the proposed approach will detect the forgery or genuine signature using filter matching method. Experimental results shows the performance of our proposed approach.
References
- Bastys, A., Kranauskas, J., &Krüger, V. (2011). Iris recognition by fusing different representations of multi-scale Taylor expansion. Computer Vision and Image Understanding, 115(6), 804-816.
- Bastys, A., Kranauskas, J., &Masiulis, R. (2009). Iris recognition by local extremum points of multiscale Taylor expansion. Pattern recognition, 42(9), 1869-1877.
- Bhattacharya, I., Ghosh, P., & Biswas, S. (2013). Offline signature verification using pixel matching technique. Procedia Technology, 10, 970-977.
- Chen, S., & Srihari, S. (2005, August). Use of exterior contours and shape features in off-line signature verification. In Eighth International Conference on Document Analysis and Recognition (ICDAR'05) (pp. 1280-1284). IEEE.
- Guerbai, Y., Chibani, Y., &Hadjadji, B. (2015). The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters. Pattern Recognition, 48(1), 103-113.
- Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification.
- Kalera, M. K., Srihari, S., & Xu, A. (2004). Offline signature verification and identification using distance statistics. International Journal of Pattern Recognition and Artificial Intelligence, 18(07), 1339-1360.
- Kruthi, C., &Shet, D. C. (2014, January). Offline signature verification using support vector machine. In 2014 Fifth International Conference on Signal and Image Processing (pp. 3-8). IEEE.
- Kumar, M. M., &Puhan, N. B. (2014, July). Inter-point envelope based distance moments for offline signature verification. In 2014 International Conference on Signal Processing and Communications (SPCOM) (pp. 1-6). IEEE.
- Kumar, R., Kundu, L., Chanda, B., & Sharma, J. D. (2010, December). A writer-independent off-line signature verification system based on signature morphology. In Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia (pp. 261-265). ACM.
- Kumar, R., Sharma, J. D., &Chanda, B. (2012). Writer-independent off-line signature verification using surroundedness feature. Pattern recognition letters, 33(3), 301-308.
- Shekar, B. H., &Bharathi, R. K. (2011, June). Eigen-signature: A robust and an efficient offline signature verification algorithm. In 2011 International Conference on Recent Trends in Information Technology (ICRTIT) (pp. 134-138). IEEE.
- Shekar, B. H., Bharathi, R. K., & Pilar, B. (2013, December). Local morphological pattern spectrum based approach for off-line signature verification. In International Conference on Pattern Recognition and Machine Intelligence (pp. 335-342). Springer, Berlin, Heidelberg.
- Shekar, B. H., Pilar, B., & Sunil, K. D. S. (2017). Blockwise binary pattern: a robust and an efficient approach for offline signature verification. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 227.
- Shikha, P., &Shailja, S. (2013). Neural network based offline signature recognition and verification system. Research Journal of Engineering Sciences ISSN, 2278, 9472.
- Soleimani, A., Araabi, B. N., &Fouladi, K. (2016). Deep multitask metric learning for offline signature verification. Pattern Recognition Letters, 80, 84-90.
- Yilmaz, M. B., Yanikoglu, B., Tirkaz, C., &Kholmatov, A. (2011, October). Offline signature verification using classifier combination of HOG and LBP features. In 2011 International Joint Conference on Biometrics (IJCB) (pp. 1-7). IEEE.
- Parameshachari, B. D., & Kumar, D. S. (2022, October). SVM Based Brain Tumor Detection and Classification System. In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) (pp.1-4).IEEE.
Index Terms
Keywords