Gender Classification Based on Online Signature Features using Machine Learning Techniques
Keywords:
Biometric Data Analysis, Gender Classification, Online Handwritten Signature, Feed Forward Deep Neural NetworkAbstract
A human signature gives a lot of insights into an individual’s characteristics such as illness, professional choices, and mood. From the biometric perspective, a Handwritten Signature is a behavioral trait and Gender is a demographic category (soft trait) of the person. Gender classification from handwritten signatures has been implied in several applications such as psychology and forensics. Male writings with a high intra-class variation tend to have a feminist aesthetic aspect, and vice versa. This gives clues to recognize the gender of the person using a handwritten signature. The proposed methodology is based on extracting numeric features from the male and female dynamic signature samples. Data was collected from 535 individuals of different age groups (18-65). Further, these signature samples were converted to numeric attributes resulting in 66 signature features from each data. Experiments were carried out using six different Machine Learning techniques; On the whole, the overall accuracy of these methods is 81.2% (KNN), 81.9% (LR), 77.1% and 49.3% (for both Poly and RBF kernels in SVM, respectively), Poly kernel using cross-validation resulted in 81.8% in SVM, 89.3% (DT), 96.2% (RF) and 98.2% (DL). Overall, the deep neural networks outperformed other models, immediately followed by RF.
Downloads
References
Gornale, S. S., Kumar, S., Patil, A., & Hiremath, P. S. (2021). Behavioral Biometric Data Analysis for Gender Classification Using Feature Fusion and Machine Learning. Frontiers in Robotics and AI, 8. https://doi.org/10.3389/frobt.2021.685966
Hiremath, P.S., & Hiremath, M. (2013). Radon Transform And Symbolic Linear Discriminant Analysis Based 3d Face Recognition Using KNN And SVM. DOI: http://warse.org/pdfs/2013/ijacst022122013.pdf
Travieso, C. M., Alonso, J. B., Vasquez, J. L., Dutta, M. K., & Singh, A. (2017). Applying forensic features on writer identification. In 2017 4th International Conference on Signal Processing and Integrated Networks, SPIN 2017 (pp. 572–577). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/SPIN.2017.8050015
Zois, E. N., Theodorakopoulos, I., Tsourounis, D., & Economou, G. (2017). Parsimonious Coding and Verification of Offline Handwritten Signatures. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (Vol. 2017-July, pp. 636–645). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2017.92
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., & Ortega-Garcia, J. (2021). DeepSign: Deep On-Line Signature Verification. IEEE Transactions on Biometrics, Behavior, and Identity Science, 3(2), 229–239. https://doi.org/10.1109/TBIOM.2021.3054533
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2019). Do you need more data? the DeepSignDB on-line handwritten signature biometric database. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR (pp. 1143–1148). IEEE Computer Society. https://doi.org/10.1109/ICDAR.2019.00185
Sharma, V., Bains, M., Verma, R., Verma, N., & Kumar, R. (2021). Novel use of logistic regression and likelihood ratios for the estimation of gender of the writer from a database of handwriting features. Australian Journal of Forensic Sciences. https://doi.org/10.1080/00450618.2021.1956587
Bay Ayzeren, Y., Erbilek, M., & Celebi, E. (2019). Emotional State Prediction from Online Handwriting and Signature Biometrics. IEEE Access, 7, 164759–164774. https://doi.org/10.1109/ACCESS.2019.2952313
AbdAli, S., & Putz-Leszczynska, J. (2014). Age and gender-invariant features of handwritten signatures for verification systems. In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2014 (Vol. 9290, p. 929021). SPIE. https://doi.org/10.1117/12.2075160
Dhieb, T., Njah, S., Boubaker, H., Ouarda, W., Ayed, M. B., & Alimi, A. M. (2018). An extended beta-elliptic model and fuzzy elementary perceptual codes for online multilingual writer identification using deep neural network. ArXiv. Computer Vision and Pattern Recognition.
Liwicki, M., Schlapbach, A., Loretan, P., & Bunke, H. (2007). Automatic detection of gender and handedness from on-line handwriting. Proc. 13th Conf. of the Graphonomics Society.
Li, N., Liu, J., Li, Q., Luo, X., & Duan, J. (2016). Online signature verification based on biometric features. In Proceedings of the Annual Hawaii International Conference on System Sciences (Vol. 2016-March, pp. 5527–5534). IEEE Computer Society. https://doi.org/10.1109/HICSS.2016.683
Durrani, M. Y., Khan, S., & Khalid, S. (2019). VerSig: a new approach for online signature verification. Cluster Computing, 22, 7229–7239. https://doi.org/10.1007/s10586-017-1129-4
Kutzner, T., Pazmiño-Zapatier, C. F., Gebhard, M., Bönninger, I., Plath, W. D., & Travieso, C. M. (2019). Writer identification using handwritten cursive texts and single character words. Electronics (Switzerland), 8(4). https://doi.org/10.3390/electronics8040391
Vorugunti, C. S., Mukherjee, P., & Pulabaigari, V. (2020). Online Signature Profiling using Generative Adversarial Networks. In 2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020 (pp. 894–896). Institute of Electrical and Electronics EngineersInc.https://doi.org/10.1109/COMSNETS48256.2020.9027369
Zhou, Y., Zheng, J., Hu, H., & Wang, Y. (2021). Handwritten Signature Verification Method Based on Improved Combined Features. Applied Sciences, 11(13), 5867. https://doi.org/10.3390/app11135867
Zhang, X. Y., Xie, G. S., Liu, C. L., & Bengio, Y. (2017). End-to-End Online Writer Identification with Recurrent Neural Network. IEEE Transactions on Human-Machine Systems, 47(2),285-292. https://doi.org/10.1109/THMS.2016.2634921
S, R., & M, H. (2017). Determining the Degree of Knowledge Processing in Semantics through Probabilistic Measures. International Journal of Information Technology and Computer Science, 9(7), 35–41. https://doi.org/10.5815/ijitcs.2017.07.04
Gornale, S., Patil, A., & Hangarge, M. (2021). Palmprint Biometric Data Analysis for Gender Classification Using Binarized Statistical Image Feature Set (pp. 157–167). https://doi.org/10.1007/978-981-16-1681-5_10
R, K., Patil, A., & Gornale, S. (2019). Fusion of Features and Synthesis Classifiers for Gender Classification using Fingerprints. International Journal of Computer Sciences and Engineering, 7(5), 526–533. https://doi.org/10.26438/ijcse/v7i5.526533
Siddalingappa, R., & Kanagaraj, S. (2022). K-nearest-neighbor algorithm to predict the survival time and classification of various stages of oral cancer: a machine learning approach. F1000Research, 11, 70. https://doi.org/10.12688/f1000research.75469.1
Safara, F., Mohammed, A. S., Yousif Potrus, M., Ali, S., Tho, Q. T., Souri, A., … Hosseinzadeh, M. (2020). An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network. IEEE Access, 8, 48428–48437. https://doi.org/10.1109/ACCESS.2020.2973509
Gornale, S.S., Patil, A., & Prabha (2016). Statistical Features Based Gender Identification Using SVM. International Journal for Scientific Research and Development, 241-244.
Rashmi, S., Hanumanthappa, M., & Jyothi, N. M. (2016). Text-to-Speech translation using Support Vector Machine, an approach to find a potential path for human-computer speech synthesizer. In Proceedings of the 2016 IEEE International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2016 (pp. 1311–1315). Presses Polytechniques Et Universitaires Romandes. https://doi.org/10.1109/WiSPNET.2016.7566349
Singh, A., N., M., & Lakshmiganthan, R. (2017). Impact of Different Data Types on Classifier Performance of Random Forest, Naïve Bayes, and K-Nearest Neighbors Algorithms. International Journal of Advanced Computer Science and Applications, 8(12). https://doi.org/10.14569/ijacsa.2017.081201
Hussein, S., Farouk, M., & Hemayed, E. S. (2019). Gender identification of egyptian dialect in twitter. Egyptian Informatics Journal, 20(2), 109–116. https://doi.org/10.1016/j.eij.2018.12.002
Shiva Shankar, Raghaveni, J., Rudraraju, P., & Vineela Sravya, Y. (2020). Classification of gender by voice recognition using machine learning algorithms. Journal of Critical Reviews, 7(9), 1217–1229. https://doi.org/10.31838/jcr.07.09.222
S, S., N, K., & Natarajan, S. (2011). Feed Forward Neural Network Based Eye Localization and Recognition Using Hough Transform. International Journal of Advanced Computer Science and Applications, 2(3). https://doi.org/10.14569/ijacsa.2011.020318
Fayyaz, M., Yasmin, M., Sharif, M., & Raza, M. (2021). J-LDFR: joint low-level and deep neural network feature representations for pedestrian gender classification. Neural Computing and Applications, 33(1), 361–391. https://doi.org/10.1007/s00521-020-05015-1
Wong, T. T., & Yeh, P. Y. (2020). Reliable Accuracy Estimates from k-Fold Cross Validation. IEEE Transactions on Knowledge and Data Engineering, 32(8), 1586–1594. https://doi.org/10.1109/TKDE.2019.2912815
Berrar, D. (2018). Cross-validation. In Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics (Vol. 1–3, pp. 542–545). Elsevier. https://doi.org/10.1016/B978-0-12-809633-8.20349-X
Probst, P., Boulesteix, A. L., & Bischl, B. (2019). Tunability: Importance of hyperparameters of machine learning algorithms. Journal of Machine Learning Research, 20.
Rashmi, S., & Hanumanthappa, M. (2017). Qualitative and quantitative study of syntactic structure: a grammar checker using part of speech tags. International Journal of Information Technology (Singapore), 9(2), 159–166. https://doi.org/10.1007/s41870-017-0016-9
Siddalingappa, R., & Kanagaraj, S. (2021). Anomaly Detection on Medical Images using Autoencoder and Convolutional Neural Network. International Journal of Advanced Computer Science and Applications, 12(7), 148–156. https://doi.org/10.14569/IJACSA.2021.0120717
Gornale, S. S., Kumar, S., & Hiremath, P. S. (2021). Handwritten Signature Biometric Data Analysis for Personality Prediction System Using Machine Learning Techniques. Transactions on Machine Learning and Artificial Intelligence, 9(5), 1–22. https://doi.org/10.14738/tmlai.95.10808
Wacom. [Online]. Available: http://www.wacom-europe.com
Gornale, S., Kumar, S., Siddalingappa, R., & Hiremath, P. S. (2022). Survey on Handwritten Signature Biometric Data Analysis for Assessment of Neurological Disorder using Machine Learning Techniques. Transactions on Machine Learning and Artificial Intelligence, 10(2), 27–60. https://doi.org/10.14738/tmlai.102.12210
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Sathish Kumar, Shivanand S. Gornale, Rashmi Siddalingappa, Arjun Mane
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
