Detection of Personality Features From Handwriting By Machine Learning Methods

Hilal Müsevitoğlu; Ali Öztürk; Fatiha Nur Başünal

EN TR

Detection of Personality Features From Handwriting By Machine Learning Methods

Abstract

Handwriting contains a lot of information about the person who wrote it. Handwriting is a sign of personality traits represented by neurological patterns in the brain. In other words, our brain and subconscious actually shape our character as a result of our habits. It is possible to get an idea about the mood of the individual by examining the handwriting. Joy, sadness, anger and anxiety are some of them. In this study, a dataset was created from the writings of people in different professions and age groups, and this dataset was applied to machine learning algorithms after the application of necessary image processing methods for feature extraction. The results of the personality analysis were compared with the results of the personality test provided by the expert psychologist.

Keywords

Handwriting Analysis , Machine Learning , Graphology

References

[1] E. W. Robertson, Fundamentals of Document Examination, Chicago: Nelson-Hall Publishers, 1991.
[2] G. Sheikholeslami, S. N. Srihari, and V. Govindaraju. "Computer aided graphology," Master's thesis, State University of New York, Buffalo, 1995.
[3] H. N. Champa and K. R. AnandaKumar, "Automated Human Behavior Prediction through Handwriting Analysis," 2010 First International Conference on Integrated Intelligent Computing, Bangalore, India, pp. 160-165, 2010. doi:10.1109/ICIIC.2010.29
[4] S. H. Fatimah, E. C. Djamal, R. Ilyas and F. Renaldi, "Personality Features Identification from Handwriting Using Convolutional Neural Networks," 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, pp. 119-124, 2019. doi:10.1109/ICITISEE48480.2019.9003855.
[5] O. Santana, C. M. Travieso, J. B. Alonso and M. A. Ferrer, "Writer identification based on graphology techniques," in IEEE Aerospace and Electronic Systems Magazine, vol. 25, no. 6, pp. 35-42, June 2010. doi:10.1109/MAES.2010.5525319
[6] J. L. Vásquez, C. M. Travieso and J. B. Alonso, "Using calligraphies features for off line writer identification," 2013 47th International Carnahan Conference on Security Technology (ICCST), Medellin, Colombia, pp. 1-6, 2013. doi:10.1109/CCST.2013.6922062.
[7] A. H. Garoot, M. Safar and C. Y. Suen, "A Comprehensive Survey on Handwriting and Computerized Graphology," 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan, pp. 621-626, 2017. doi:10.1109/ICDAR.2017.107
[8] A. Anand, D. Patil, S. Bhaawat, S. Karanje and V. Mangalvedhekar, "Automated Career Guidance Using Graphology, Aptitude Test and Personality Test," 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, August 2018, pp. 1-5, doi: 10.1109/ICCUBEA.2018.8697642
[9] V. Patil and H. Mathur, "A Survey: Machine Learning Approach for Personality Analysis and Writer Identification through Handwriting," 2020 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, pp. 1-5, February 2020. doi:10.1109/ICICT48043.2020.9112449
[10] H. Bacanlı, T. İlhan and S. Aslan, “Beş faktör kuramina dayali bir kişilik ölçeğinin geliştirilmesi: sifatlara dayali kişilik testi (SDKT)”. The Journal of Turkish Educational Sciences, vol. 7, no. 2, pp. 261-279, January 2009.

[11] J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, 1993.
[12] Z. Wu, W. Lin, Z. Zhang, A. Wen and L. Lin, "An Ensemble Random Forest Algorithm for Insurance Big Data Analysis," 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), Guangzhou, China, pp. 531-536, 2017. doi:10.1109/CSE-EUC.2017.99
[13] F. Rosenblatt, “The perceptron: A probabilistic model for information storage and organization in the brain,” Psychological Review, vol. 65, no. 6, pp. 386–408, 1958. doi:10.1037/h0042519
[14] B. E. Boser, I. Guyon and V. N. Vapnik, “A Training Algorithm for Optimal Margin Classifiers,” Proceedings of the 5th Annual Workshop on Computational Learning Theory (COLT’92), Pittsburgh, pp. 144-152 July 1992. doi:10.1145/130385.130401
[15] J. Tolles and W. Meurer, “Logistic Regression: Relating Patient Characteristics to Outcomes.,” JAMA, vol. 316, no. 5, pp. 533–534. August 2016. doi:10.1001/jama.2016.7653
[16] T. Chen, C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794, August 2016. doi:10.1145/2939672.2939785
[17] K. Guolin, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye and T. Liu, “LightGBM: A Highly Efficient Gradient Boosting Decision Tree,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 3146–3154, December 2017.
[18] D. Zhang and Y. Gong, "The Comparison of LightGBM and XGBoost Coupling Factor Analysis and Prediagnosis of Acute Liver Failure," in IEEE Access, vol. 8, pp. 220990-221003, December 2020. doi:10.1109/ACCESS.2020.3042848.
[19] A. Shehadeh, O. Alshboul, R.E. Al Mamlook, O. Hamedat, "Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression," Automation in Construction, vol. 129, pp. 1-16, September 2021. doi:10.1016/j.autcon.2021.103827
[20] E. C. Djamal, R. Darmawati and S. N. Ramdlan, "Application image processing to predict personality based on structure of handwriting and signature," 2013 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), Jakarta, Indonesia, pp. 163-168, November 2013. doi:10.1109/IC3INA.2013.6819167
[21] B. Fallah and H. Khotanlou, "Identify human personality parameters based on handwriting using neural network," 2016 Artificial Intelligence and Robotics (IRANOPEN), Qazvin, Iran, pp. 120-126, April 2016. doi:10.1109/RIOS.2016.7529501.
[22] A. Sen and H. Shah, "Automated handwriting analysis system using principles of graphology and image processing," 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, pp. 1-6, March 2017. doi:10.1109/ICIIECS.2017.8276061.
[23] M. Gavrilescu and N. Vizireanu, “Predicting the Big Five personality traits from handwriting,” EURASIP Journal on Image and Video Processing, vol. 57, July 2018. doi:10.1186/s13640-018-0297-3vol
[24] A. Chitlangia and G. Malathi, “Handwriting Analysis based on Histogram of Oriented Gradient for Predicting Personality traits using SVM,” Procedia Computer Science, vol 165, pp. 384-390, 2019. doi:10.1016/j.procs.2020.01.034
[25] A. Saraswal and U. R. Saxena, "Personality Trait Prediction Using Handwriting Recognition with KNN," 2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), Greater Noida, India, pp. 551-555, May 2022. doi:10.1109/CISES54857.2022.9844344
[26] D. Alamsyah, Samsuryadi, W. Widhiarso and S. Hasan, "Handwriting Analysis for Personality Trait Features Identification using CNN,” 2022 International Conference on Data Science and Its Applications (ICoDSA), Bandung, Indonesia, pp. 232-238, July 2022. doi:10.1109/ICoDSA55874.2022.9862910

Details

Primary Language

English

Subjects

Computer Software

Journal Section

Conference Paper

Authors

Hilal Müsevitoğlu ^*
0000-0001-5791-790X
Türkiye

Ali Öztürk
0000-0002-1797-2039
Türkiye

Fatiha Nur Başünal
0000-0002-0896-1194
Türkiye

Publication Date

August 31, 2023

Submission Date

January 25, 2023

Acceptance Date

June 16, 2023

Published in Issue

Year 2023 Volume: 9 Number: 2

IZ

https://izlik.org/JA92YE54PC

IEEE

[1]H. Müsevitoğlu, A. Öztürk, and F. N. Başünal, “Detection of Personality Features From Handwriting By Machine Learning Methods”, GJES, vol. 9, no. 2, pp. 200–212, Aug. 2023, [Online]. Available: https://izlik.org/JA92YE54PC

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