Abstract
As an important research direction in the field of biometrics, offline signature verification plays an important role. This paper proposes BoVW based on feature selection algorithm MRMR for offline signature verification. In this paper, eigenvectors were formed by extracting visual word features and the features were obtained by building a visual word bag of signature samples. In order to improve the relevance between eigenvectors and categories, and reduce the redundancy between features, the Maximum Relevance and Minimum Redundancy algorithm was used to select features of visual word eigenvectors. The algorithm can find the optimal feature subset. The experiments were conducted using 1200 samples from in our Uyghur signature database, and comparison experiments were carried on selecting 2640 samples from CEDAR database. It was obtained 93.81% of ORR from Uyghur signature and 95.38% of ORR using Latin signature from CEADER database respectively. The experimental results indicated the efficiency of proposed method in this paper.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Maergner, P., Riesen, K., Ingold, R., et al.: Offline signature verification based on bipartite approximation of graph edit distance. In: International Graphonomics Society Conference (2017)
Ma, X., Sang, Q.: Handwritten signature verification algorithm based on LBP and deep learning. Chin. J. Quant. Electron. 34(1), 23–31 (2017)
Zois, E.N., Alewijnse, L., Economou, G.: Offline signature verification and quality characterization using poset-oriented grid features. Pattern Recogn. 54(C), 162–177 (2016)
Okawa, M.: Synergy of foreground-background images for feature extraction: offline signature verification using fisher vector with fused KAZE features. Pattern Recogn. 79, 480–489 (2018)
Kumar, R.: Signature verification using support vector machine (SVM). Int. J. Sci. Res. Manage. Stud. 4(6), 1771–1773 (2017)
Hafemann, L.G., Sabourin, R., Oliveira, L.S., et al.: Offline handwritten signature verification-literature review. In: International Conference on Image Processing Theory (2017)
Ubul, K., Yibulayin, T., Mahpirat: Uyghur off-line signature verification based on modified corner line features. In: 2016 International Conference on Artificial Intelligence and Computer Science (AICS 2016), pp. 465–469 (2016)
Ubul, K., Abudurexiti, R., Mamat, H., Yadikar, N., Yibulayin, T.: Uyghur off-line signature recognition based on modified corner curve features. In: You, Z., et al. (eds.) CCBR 2016. LNCS, vol. 9967, pp. 417–423. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46654-5_46
Ubul, K., Zhu, Y.-l., Mamut, M., Yadikar, N., Yibulayin, T.: Uyghur off-line signature recognition based on local central line features. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 750–758. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69923-3_80
Gheni, Z., Mahpirat, N.Y., Ubul, K.: Uyghur off-line signature verification based on the directional features. J. Image Signal Process. 6(2), 121–129 (2017)
Yimin, A., Mamut, M., Aysa, A., et al.: High-dimensional statistical features based uyghur handwritten signature recognition. J. Front. Comput. Sci. Technol., 308–317 (2017)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: Computer Vision and Pattern Recognition Workshop on Generative-Model Based Vision (2004)
Lowe, D.G.: Distinctive image feature from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–100 (2004)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)
Chauhan, V.K., Dahiya, K., Sharma, A.: Problem formulations and solvers in linear SVM: a review. Artif. Intell. Rev. 6, 1–53 (2018)
Serdouk, Y., Nemmour, H., Chibani, Y.: New gradient features for off-line handwritten signature verification. In: International Symposium on Innovations in Intelligent Systems and Applications, pp. 1–4. IEEE (2015)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 61363064, 61563052, 61163028).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, SJ., Mahpirat, Aysa, Y., Ubul, K. (2018). BoVW Based Feature Selection for Uyghur Offline Signature Verification. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_74
Download citation
DOI: https://doi.org/10.1007/978-3-319-97909-0_74
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97908-3
Online ISBN: 978-3-319-97909-0
eBook Packages: Computer ScienceComputer Science (R0)