Ensemble Learning Based Analysis Correlating Graphology to Big Five Personality Model

Ensemble Learning Based Analysis Correlating Graphology to Big Five Personality Model

© 2022 by IJETT Journal
Volume-70 Issue-1
Year of Publication : 2022
Authors : Lakshmi Durga, Deepu. R
DOI : 10.14445/22315381/IJETT-V70I1P229

How to Cite?

Lakshmi Durga, Deepu. R, "Ensemble Learning Based Analysis Correlating Graphology to Big Five Personality Model," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 254-265, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I1P229

Graphology and the Big five personality model are two different streams for predicting an individual’s personality. Though their mechanisms are different, both culminate in the same goal of personality assessment. Big Five is the standardized model and uses the responses of 44 item questionnaire to categorize the personality of the individual in terms of scores for five traits of openness, conscientiousness, extraversion, agreeableness and neuroticism. Graphology is not standardized, and it uses handwriting traits to predict certain personality traits. This research work extends graphological concepts to fit into the big five model personality classifications through the convergence of image processing and machine learning. A clustering-based analysis is made to correlate the graphological features and big five personality observations. From the analysis, an ensemble learning classifier model is built for big five personality traits prediction from graphological features.

Machine Learning, Clustering, Graphology, Handwriting Traits, Big Five Personality.

[1] John, O. P., & Srivastava, S., The Big-Five trait taxonomy, History, measurement, and theoretical perspectives. In L. A. Pervin& O. P. John (Eds.), Handbook of personality, Theory and research, New York, Guilford Press, 2 (1999) 102–138.
[2] Lakshmi Durga, R Deepu, Handwriting Analysis Through Graphology, A Review, International Conference on Advances in Computing, Communications and Informatics (ICACCI), (2018)
[3] B Fallah, H Khotanlou, Identify human personality parameters based on handwriting using neural networks, in Artificial Intelligence and Robotics (IRAN OPEN). (2016)
[4] Mekhaznia, T., Djeddi, C., & Sarkar, S. (2021), Personality Traits Identification Through Handwriting Analysis, Pattern Recognition and Artificial Intelligence, 4th Mediterranean Conference, MedPRAI, Hammamet, Tunisia, Proceedings, 1322 (2020) 155–169. https,//doi.org/10.1007/978-3-030-71804-6_12
[5] Mutalib, S., Rahman, S.A., Yusoff, M., Mohamed, A Personality analysis based on letter ‘t’ using backpropagation neural network, In Proceedings of the International Conference on Electrical Engineering and Informatics Institut Teknologi Bandung (2007).
[6] Gavrilescu, M., Vizireanu, N, Predicting the Big Five personality traits from handwriting, J Image Video Proc. 57 (2018).
[7] Mishra A, Forensic Graphology, Assessment of Personality. Forensic Res Criminol, Int J., 4(1) (2017) 9-12. DOI, 10.15406/frcij.2017.04.00097
[8] Asra, S., Shubhangi, D.C, Human behaviour recognition based on handwritten cursives by SVM classifier, In, International Conference on Electrical, Electronics, Communication Computer Technologies and Optimization Techniques, ICEECCOT (2017) (2018). 10.1109/ICEECCOT.2017.8284679
[9] H.N. Champa, K.R.AnandaKumar, Artificial Neural Network for Human Behavior Prediction through Handwriting Analysis, International Journal of Computer Applications (0975 - 8887) . 2(2) (2010).
[10] A. Rahiman, D. Varghese, M. Kumar, Handwritten Analysis Based Individualistic Traits Prediction, International Journal of Image Processing (TJIP), 7(2) (2013).
[11] J. Fisher, A. Maredia, A. Nixon, N. Williams, J. Leet, IdentifYing Personality Traits, and Especially Traits Resulting in Violent Behavior through Automatic Handwriting Analysis, Proceedings of StudentFaculty Research Day, CSIS, Pace University, (2012).
[12] Sh. Prasad, V. Kumar, A. Sapre, Handwriting Analysis based on Segmentation Method for Prediction of Human Personality using Support Vector Machine, International Journal of Computer Applications (0975 - 8887) . 8(12) (2010).
[13] Grewal, Prashar, Behavior Prediction Through Handwriting Analysis, IJCST (2012).
[14] Coll, R.; Fornes, A.; Llados, J, Graphological analysis of handwritten text documents for Human Resources Recruitment, ICDAR ’09 (2009) 1081-1085.
[15] S. Mukherjee and I. De, Feature extraction from handwritten documents for personality analysis, International Conference on Computer, Electrical & Communication Engineering (ICCECE), (2016) 1-8.
[16] P. Joshi, Handwriting analysis for detection of personality traits using machine learning approach, International Journal of Computer Applications (0975 8887) . 130 (2015).
[17] R. Kacker and H. B. Maringanti, Personality analysis through handwriting, GSTF Journal on Computing (JoC), 2 (2012) 94–97.
[18] S. Mutalib, R. Ramli, S. A. Rahman, M. Yusoff and A. Mohamed, Towards emotional control recognition through handwriting using fuzzy inference, 2008 International Symposium on Information Technology, (2008) 1-5
[19] Wijaya, Waskitha& Tolle, Herman &Utaminingrum, Fitri., Personality Analysis through Handwriting Detection Using Android Based Mobile Device, Journal of Information Technology and Computer Science. 2 (2013). 10.25126/jitecs.20172237.
[20] Chitlangia, Aditya &Malathi, G., Handwriting Analysis based on Histogram of Oriented Gradient for Predicting Personality traits using SVM, Procedia Computer Science. 165 (2019) 384-390. 10.1016/j.procs.2020.01.034.
[21] Pratiwi D, Santoso GB, Saputri FH, Personality type assessment system by using enneagram-graphology techniques on digital handwriting, International Journal of Computer Applications 147(11) (2016).
[22] N. Majumder, S. Poria, A. Gelbukh and E. Cambria, Deep Learning-Based Document Modeling for Personality Detection from Text, in IEEE Intelligent Systems, 32(2) (2017) 74-79.
[23] Lokhande VR, Gawali BW, Analysis of signature for the prediction of personality traits, In Intelligent Systems and Information Management (ICISIM), 1st International Conference on. IEEE; (2017) 44–9.
[24] Hashemi S, Vaseghi B, Torgheh F, Graphology for Farsi handwriting using image processing techniques, IOSR J Electron CommunEng(IOSR-JECE) 10(3) (2015) 01–7.
[25] Deepu, R; Murali, S; Raju, Vikram, A Mathematical model for the determination of the distance of an object in a 2D image, Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV); Athens, Athens, The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)(2013) 1-5.
[26] Lakshmi Durga Deepu R. Ensemble Deep Learning to Classify Specific Types of t and i Patterns in Graphology, Global Transitions Proceedings, (2021).
[27] K. He, X. Zhang, S. Ren and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition,(CVPR), (2016) 770-778
[28] Sitansu Kumar Das, Sanjoy Kumar Saha, Dipti Prasad Mukherjee Multiple Objects Segmentation with Fuzzy Rule-Base Trained Topology Adaptive Active Membrane, ICVGIP ’10, Chennai, India Copyright 2010 ACM 978-1-4503-0060-5/10/12. (2010).
[29] https,//openpsychometrics.org/tests/IPIP-BFFM/