Abstract
Handwriting is an expressive behavior and communicates one’s personality characteristics. In this work, we try to build a correlation between personality traits and handwriting features employing machine learning techniques. Quantitative analysis is done for correlating handwritten features and personality traits. Two datasets are created for the work, personality trait value dataset and handwriting feature value dataset. The Big Five personality assessment model along with BFI questionnaire are used to collect personality data. It is observed that in handwriting samples of a person, there is a consistency of certain features in respect of graphometric measurements which are both repeatable and reliable. We utilize the measurements related to four lowercase letters of English handwritten script, namely ‘a’, ‘g’, ‘n’ and ‘t’, and the features of the word ‘of’ as well. Personality trait prediction from handwriting features is formulated as a multi-label classification problem. Each of the five traits corresponds to a class in the classifier. Both non-binary and binary label values are examined by using three multi-label classifiers, Classifier Chain (CC), Binary Relevance (BR) and Label-Power set (LP). We employ KSTAR, K-nearest neighbor and Multi-layer Perceptron (MLP) as base classifiers. CC-KSTAR (94.1% with non-binary label values) and CC/LP-KSTAR (97.6% with binary label values) provide the best result considering train-test (67:33) as test option. CC-KNN (ED) (98.1% with non-binary label values) displays the best result considering cross validation as a test option.
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Acknowledgements
We express our gratitude to Prof. Kaushik Roy of West Bengal State University, Barasat, India, for guiding us to get the ethical clearance for our work.
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Mukherjee, S., Ghosh, I.D., Mukherjee, D. (2022). Big Five Personality Prediction from Handwritten Character Features and Word ‘of’ Using Multi-label Classification. In: Giri, D., Raymond Choo, KK., Ponnusamy, S., Meng, W., Akleylek, S., Prasad Maity, S. (eds) Proceedings of the Seventh International Conference on Mathematics and Computing . Advances in Intelligent Systems and Computing, vol 1412. Springer, Singapore. https://doi.org/10.1007/978-981-16-6890-6_21
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