Feature Ranking Using Novel Consistency Measure by Normalized Standard Deviation and Proposal of Three Novel Global Features for Online Signature Verification

Biswajit Kar; Bhimraj Prasai Chetry; Sayan Das

doi:10.52756/ijerr.2024.v44spl.016

Authors

Biswajit Kar Department of Instrumentation Engineering, Central Institute of Technology Kokrajhar, Assam, India https://orcid.org/0000-0003-2686-5814
Bhimraj Prasai Chetry Department of Instrumentation Engineering, Central Institute of Technology Kokrajhar, Assam, India https://orcid.org/0009-0002-5072-4739
Sayan Das Department of Instrumentation Engineering, Central Institute of Technology Kokrajhar, Assam, India https://orcid.org/0009-0006-4644-9438

DOI:

https://doi.org/10.52756/ijerr.2024.v44spl.016

Keywords:

Consistency estimation, Feature extraction, Feature normalization, Feature ranking, Feature selection, mRMR

Abstract

Signature is a behavioral biometric that evolves throughout a person’s life. Feature extraction and ranking are very important steps towards online signature verification in order to achieve high efficiency. In our case, we have extracted 48 global features. A novel feature ranking technique based on consistency measure using normalized standard deviation is proposed here and is compared with well-established mRMR based ranking. The use of normalized standard deviation formula is the novel approach for feature ranking. Moreover, we have proposed three novel global features for consistency measure and mRMR based ranking. These three features has shown its importance in consistency based ranking as well as in mRMR based ranking. Consistency estimation of global features is important in one-class classification framework, where only the genuine signatures are available. All the features that are ranked by consistency measures and its weighting factors of feature vector are computed for every signer. More consistent features has given more weight for verification. Feature ranking shows the importance of each features for a particular system. Also it helps in feature selection to select the more discriminating features for a particular system and removing all irrelevant features. It saves the computational time and size of the model. Different global features has different scaling factor. Generally, normalization is done before ranking but in our technique we have ranked our features without normalization. Because normalization disrupt the statistical consistency. Therefore, we have used min-max normalization where all the features are converted to (0-1) range after ranking. In our proposed system, the proposed phase related global features shows more consistency in our ranking process. Similarly well-established mRMR feature ranking has also ranked our proposed novel features number 48 (Standard deviation of the phase), 43 (Entropy of shape signature function) and 47 (Mean of phase) within top 13 features. It is seen that both our proposed novel feature ranking technique based on consistency measure and well-established mRMR based ranking has shown almost similar performance. The proposed algorithms are verified with SVC 2004 database.

References

Anggoro, D. A. (2019). Improving accuracy by applying Z-score normalization in linear regression and polynomial regression models for real estate data. International Journal of Emerging Trends in Engineering Research, 7(11), 549-555.

https://doi.org/10.30534/ijeter/2019/247112019 DOI: https://doi.org/10.30534/ijeter/2019/247112019

Cabello-Solorzano, K., Ortigosa de Araujo, I., Peña, M., Correia, L., & J. Tallón-Ballesteros, A. (2023). The impact of data normalization on the accuracy of machine learning algorithms: A comparative analysis. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023), 344-353.

https://doi.org/10.1007/978-3-031-42536-3_33 DOI: https://doi.org/10.1007/978-3-031-42536-3_33

Chetry, B. P., & Kar, B. (2024). Kruskal Wallis and mRMR Feature Selection based Online Signature Verification System using Multiple SVM and KNN. International Journal of Experimental Research and Review, 42, 298–311.

https://doi.org/10.52756/ijerr.2024.v42.026 DOI: https://doi.org/10.52756/ijerr.2024.v42.026

Guru, D., & Prakash, H. (2009). Online signature verification and recognition: An approach based on symbolic representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(6), 1059-1073. https://doi.org/10.1109/tpami.2008.302 DOI: https://doi.org/10.1109/TPAMI.2008.302

Hanchuan Peng, Fuhui Long, & Ding, C. (2005). Feature selection based on mutual information criteria of Max-dependency, Max-relevance, and MIN-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226-1238. https://doi.org/10.1109/tpami.2005.159 DOI: https://doi.org/10.1109/TPAMI.2005.159

Herwanto, H. W., Handayani, A. N., Wibawa, A. P., Chandrika, K. L., & Arai, K. (2021). Comparison of Min-Max, Z-score and decimal scaling normalization for zoning feature extraction on Javanese character recognition. 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE). https://doi.org/10.1109/iceeie52663.2021.9616665 DOI: https://doi.org/10.1109/ICEEIE52663.2021.9616665

Jain, A., Nandakumar, K., & Ross, A. (2005). Score normalization in multimodal biometric systems. Pattern Recognition, 38(12), 2270-2285. https://doi.org/10.1016/j.patcog.2005.01.012 DOI: https://doi.org/10.1016/j.patcog.2005.01.012

Kar, B., & K. Dutta, P. (2012). SVM based signature verification by fusing global and functional features. International Journal of Computer Applications, 60(16), 34-39. https://doi.org/10.5120/9778-4359 DOI: https://doi.org/10.5120/9778-4359

Kar, B., Mukherjee, A., & Dutta, P. K. (2018). Stroke point warping-based reference selection and verification of online signature. IEEE Transactions on Instrumentation and Measurement, 67(1), 2-11. https://doi.org/10.1109/tim.2017.2755898 DOI: https://doi.org/10.1109/TIM.2017.2755898

Kashi, R. S. (1996). On?line handwritten signature verification using stroke direction coding. Optical Engineering, 35(9), 2526.

https://doi.org/10.1117/1.600857 DOI: https://doi.org/10.1117/1.600857

Leclerc, F., & Plamondon, R. (1994). Automatic signature verification: The state of the art—1989–1993. International Journal of Pattern Recognition and Artificial Intelligence, 08(03), 643-660. https://doi.org/10.1142/s0218001494000346 DOI: https://doi.org/10.1142/S0218001494000346

Lei, H., & Govindaraju, V. (2005). A comparative study on the consistency of features in on-line signature verification. Pattern Recognition Letters, 26(15), 2483-2489. https://doi.org/10.1016/j.patrec.2005.05.005 DOI: https://doi.org/10.1016/j.patrec.2005.05.005

Maiorana, E., Campisi, P., Fierrez, J., Ortega-Garcia, J., & Neri, A. (2010). Cancelable templates for sequence-based biometrics with application to on-line signature recognition. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 40(3), 525-538. https://doi.org/10.1109/tsmca.2010.2041653 DOI: https://doi.org/10.1109/TSMCA.2010.2041653

Najda, D., & Saeed, K. (2022). Impact of augmentation methods in online signature verification. Innovations in Systems and Software Engineering, 20, 477-483. https://doi.org/10.1007/s11334-022-00464-4 DOI: https://doi.org/10.1007/s11334-022-00464-4

Plamondon, R., & Srihari, S. (2000). Online and off-line handwriting recognition: A comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 63-84. https://doi.org/10.1109/34.824821 DOI: https://doi.org/10.1109/34.824821

Wang, X., Tao, Y., & Zheng, K. (2018). Feature selection methods in the framework of mRMR. 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC). https://doi.org/10.1109/imccc.2018.00307 DOI: https://doi.org/10.1109/IMCCC.2018.00307

Xia, Z., Shi, T., Xiong, N. N., Sun, X., & Jeon, B. (2018). A privacy-preserving handwritten signature verification method using Combinational features and secure KNN. IEEE Access, 6, 46695-46705. https://doi.org/10.1109/access.2018.2866411 DOI: https://doi.org/10.1109/ACCESS.2018.2866411

Yeung, D., Chang, H., Xiong, Y., George, S., Kashi, R., Matsumoto, T., & Rigoll, G. (2004). SVC2004: First international signature verification competition. Biometric Authentication, 16-22. https://doi.org/10.1007/978-3-540-25948-0_3 DOI: https://doi.org/10.1007/978-3-540-25948-0_3

Zhang, K., Pratikakis, I., Cornelis, J., & Nyssen, E. (2000). Using landmarks to establish a point-to-point correspondence between signatures. Pattern Analysis & Applications, 3(1), 69-75. https://doi.org/10.1007/s100440050007 DOI: https://doi.org/10.1007/s100440050007