https://www.grafoteca.com/biblioteca/files/original/29d3b8995f669bc2439341683b57ad49.pdf a29d7d9e6f828eed36e62f9d58a67f8c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Full text Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Predicting the Big Five personality traits from handwriting Type The nature or genre of the resource Journal Article Creator An entity primarily responsible for making the resource M. Gavrilescu N. Vizireanu Date A point or period of time associated with an event in the lifecycle of the resource July 10, 2018 Language A language of the resource English Zotero Title Predicting the Big Five personality traits from handwriting Item Type Journal Article Author M. Gavrilescu N. Vizireanu URL https://doi.org/10.1186/s13640-018-0297-3 Volume 2018 Issue 1 Pages 57 Publication Title EURASIP Journal on Image and Video Processing ISSN 1687-5281 Date July 10, 2018 Journal Abbreviation EURASIP Journal on Image and Video Processing DOI 10.1186/s13640-018-0297-3 Access Date 2019-04-24 05:22:35 Library Catalog BioMed Central Language English Abstract Note 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. Attachment Title Full text