Abstract
The Big Five Factors Model (BFFM) is the most widely accepted personality theory used by psychologists today. The theory states that personality can be described with five core factors which are Conscientiousness, Agreeableness, Emotional Stability, Openness to Experience, and Extraversion. In this work, we measure the five factors using handwriting analysis instead of answering a long questionnaire of personality test. Handwriting analysis is a study that merely needs a writing sample to assess personality traits of the writer. It started manually by interpreting the extracted features such as size of writing, slant, and space between words into personality traits based on graphological rules. In this work, we proposed an automated BFFM system called Averaging of SMOTE multi-label SVM-CNN (AvgMlSC). AvgMlSC constructs synthetic samples to handle imbalanced data using Synthetic Minority Oversampling Technique (SMOTE). It averages two learning-based classifiers i.e. Multi-label Support Vector Machine and Multi-label Convolutional Neural Network based on offline handwriting recognition to produce one optimal predictive model. The model was trained using 1066 handwriting samples written in English, French, Chinese, Arabic, and Spanish. The results reveal that our proposed model outperformed the overall performance of five traditional models i.e. Logistic Regression (LR), Naïve Bayes (NB), K-Neighbors (KN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) with 93% predictive accuracy, 0.94 AUC, and 90% F-Score.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akrami, N., Fernquist, J., Isbister, T., Kaati, L., Pelzer, B.: Automatic extraction of personality from text: challenges and opportunities. In: Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), pp. 3156–3164. IEEE (2019)
Djamal, E.C., Darmawati, R.: Recognition of human personality trait based on features of handwriting analysis using multi structural algorithm and artificial neural networks. In: Proceedings of the 2013 IEEE Conference on Control, Systems & Industrial Informatics (ICCSII), pp. 22–24 (2013)
Gavrilescu, M., Vizireanu, N.: Predicting the big five personality traits from handwriting. EURASIP J. Image Video Process. 2018(1), 1–17 (2018). https://doi.org/10.1186/s13640-018-0297-3
Giraldo-Forero, A.F., Jaramillo-Garzón, J.A., Ruiz-Muñoz, J.F., Castellanos-Domínguez, C.G.: Managing imbalanced data sets in multi-label problems: a case study with the SMOTE algorithm. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013. LNCS, vol. 8258, pp. 334–342. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41822-8_42
Majumder, N., Poria, S., Gelbukh, A., Cambria, E.: Deep learning-based document modeling for personality detection from text. IEEE Intell. Syst. 32(2), 74–79 (2017)
Ploog, H.: Handwriting Psychology: Personality Reflected in Handwriting. iUniverse (2013)
Power, R.A., Pluess, M.: Heritability estimates of the big five personality traits based on common genetic variants. Transl. Psychiatry 5(7), e604–e604 (2015)
Raut, A.A., Bobade, A.M.: Prediction of human personality by handwriting analysis based on segmentation method using support vector machine. Int. J. Pure Appl. Res. Eng. Technol. (2014)
Roman, K.G.: Handwriting: a key to personality (1954)
Salminen, J., Rao, R.G., Jung, S., Chowdhury, S.A., Jansen, B.J.: Enriching social media personas with personality traits: a deep learning approach using the big five classes. In: Degen, H., Reinerman-Jones, L. (eds.) HCII 2020. LNCS, vol. 12217, pp. 101–120. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50334-5_7
Satow, R., Rector, J.: Using gestalt graphology to identify entrepreneurial leadership. Percept. Mot. Skills 81(1), 263–270 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Garoot, A., Suen, C.Y. (2022). Measuring the Big Five Factors from Handwriting Using Ensemble Learning Model AvgMlSC. In: Carmona-Duarte, C., Diaz, M., Ferrer, M.A., Morales, A. (eds) Intertwining Graphonomics with Human Movements. IGS 2022. Lecture Notes in Computer Science, vol 13424. Springer, Cham. https://doi.org/10.1007/978-3-031-19745-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-19745-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19744-4
Online ISBN: 978-3-031-19745-1
eBook Packages: Computer ScienceComputer Science (R0)