Predicting the Big Five personality traits from handwriting

Gavrilescu and Vizireanu EURASIP Journal on Image and Video Processing (2018) 2018:57 https://doi.org/10.1186/s13640-018-0297-3 EURASIP Journal on Image and Video Processing RESEARCH Open Access Predicting the Big Five personality traits from handwriting Mihai Gavrilescu* and Nicolae Vizireanu Abstract We propose the first non-invasive three-layer architecture in literature based on neural networks that aims to determine the Big Five personality traits of an individual by analyzing offline handwriting. We also present the first database in literature that links the Big Five personality type with the handwriting features collected from 128 subjects containing both predefined and random texts. Testing our novel architecture on this database, we show that the predefined texts add more value if enforced on writers in the training stage, offering accuracies of 84.4% in intra-subject tests and 80.5% in inter-subject tests when the random dataset is used for testing purposes, up to 7% higher than when random datasets are used in the training phase. We obtain the highest prediction accuracy for Openness to Experience, Extraversion, and Neuroticism (over 84%), while for Conscientiousness and Agreeableness, the prediction accuracy is around 77%. Overall, our approach offers the highest accuracy compared with other state-of-the-art methods and results are computed in maximum 90 s, making the approach faster than the questionnaire or psychological interviews currently used for determining the Big Five personality traits. Our research also shows there are relationships between specific handwriting features and prediction with high accuracy of specific personality traits and this can be further exploited for improving, even more, the prediction accuracy of the proposed architecture. Keywords: Neural networks, Handwriting analysis, Personality classification, Feature classification 1 Introduction Handwriting has been used for centuries as a way of communication and expression for humans, but only recently its links to the brain activity and the psychological aspects of humans have been studied. The psychological study of handwriting with the purpose of determining the personality traits, psychological states, temperament, or the behavior of the writer is called graphology and is still a debatable domain as it lacks a standard, most of the handwriting interpretations being done subjectively by trained graphologists. However, there have been various research papers showing the link between handwriting and neurological aspects of humans, one such study being the one of Plamondon [1], where it was shown that the brain forms characters based on habits of writers and each neurological brain pattern forms a distinctive neuromuscular movement which is similar for individuals with the same * Correspondence: Department of Telecommunications, University “Politehnica” of Bucharest, 1-3 Iuliu Maniu Blvd, 06107 Bucharest 6, Romania type of personality. Therefore, handwriting is, from this perspective, an accurate mirror of people’s brain. Graphologists currently analyze multiple handwriting features in order to assess the psychological aspects of the writer, such as the weights of strokes [2], the trajectory of writing [3], the way the letter “t” or “y” are written [4], as well as other features related to how letters or words are written or how the text is positioned on the page. In the current paper, we aim to build the first architecture in literature that is able to automatically analyze a set of handwriting features and evaluate the personality of the writer using the Five-Factor Model (FFM). To test this architecture, we propose the first database that links the FMM personality traits to handwriting features, which is a novel aspect of this research paper. The proposed system offers an attractive alternative to the standard FMM questionnaire or psychological interviews that are currently used for evaluating personality, because it is easier to use, it involves less effort, and is faster as well as removes the subjectivity from both subject’s (as usually the subject is asked to self-report on a © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Gavrilescu and Vizireanu EURASIP Journal on Image and Video Processing (2018) 2018:57 specific questionnaire) as well as clinician’s sides (as typically psychologists are reviewing the questionnaire results and share opinions regarding the personality of the individual, opinions which can sometimes be prone to bias such that different psychologists might provide different evaluations). We show that our proposed system offers the highest accuracy compared to other state-of-the-art methods as well as share our findings regarding the relationship between several handwriting features and specific personality traits that can be further exploited to improve, even more, the accuracy of such a system. In the following section, we present the state-of-the-art in the area of handwriting analysis, focusing on papers related to predicting the psychological traits of individuals. We continue in the subsequent section with describing the two models used (FMM and graphology analysis) followed by a detailed presentation of the three-layer architecture, as well as the classifiers and the structure of the neural network used. Finally, we detail the experimental results and share our findings and conclusions on the results obtained. 2 Related work As mentioned previously, currently, there is no standard developed in predicting behavior based on handwriting, the majority of graphological analysis being done by specialized graphologists. However, research was conducted in the area of computer science which aimed to create such systems in order to recognize the behavior from handwriting in an easier way and also to standardize the graphological analysis. In the next paragraphs, we present the state-of-the-art in this area as well as several studies which made use of handwriting to determine the psychological traits or mental status of individuals. Behnam Fallah and Hassan Khotanlou describe in [5] a research with a similar purpose as the one conducted in this paper, aiming to determine the personality of an individual by studying handwriting. The Minnesota Multiphasic Personality Inventory (MMPI) is used for training their system and a Hidden Markov Model (HMM) is employed for classifying the properties related to the target writer, while a neural network (NN) approach is used for classifying the properties which are not writer-related. The handwriting image is analyzed by these classifiers (...truncated)