(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-9 Issue-93 August-2022
Full-Text PDF
Paper Title : A self adaptive cognitive deep learning framework for classifying graphology features to Big five personality traits
Author Name : Lakshmi Durga and Deepu. R
Abstract :

Graphology is a technique for study and analysis of the individual personality from his/her handwriting style. Most of the existing graphology-based solutions for personality detection recognize nonstandard application dependent personalities. Even the very few big five personality recognition approaches have limited accuracy and lacks adaptivity to new handwriting styles. Towards these problems, a novel self-adaptive cognitive learning framework based on deep learning convolutional neural network (CNN) features is proposed to classify the handwritten document to big five personality traits. This framework correlates the various document level and character level graphology features to big five personality traits to recognize features with a strong correlation to various big five personality traits and uses these features to classify the personality. To enhance the deep learning feature learning ability an enhanced convolution kernel is proposed for the CNN. Through testing with various handwritten documents, the proposed solution is found to provide 2.18% higher accuracy and 5% lower false positives compared to existing works on big five personality classification.

Keywords : Graphology, Big five personality, Deep learning, Cognitive learning.
Cite this article : Durga L, Deepu. R. A self adaptive cognitive deep learning framework for classifying graphology features to Big five personality traits. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(93):1151-1167. DOI:10.19101/IJATEE.2021.875577.
References :
[1]Durga L, Deepu R. Handwriting analysis through graphology: a review. In international conference on advances in computing, communications and informatics 2018 (pp. 1160-6). IEEE.
[Crossref] [Google Scholar]
[2]John OP, Srivastava S. The big-five trait taxonomy: history, measurement, and theoretical perspectives.
[Google Scholar]
[3]Plamondon R. Neuromuscular studies of handwriting generation and representation. In 12th international conference on frontiers in handwriting recognition 2010. IEEE.
[Crossref] [Google Scholar]
[4]Rahman AU, Halim Z. Identifying dominant emotional state using handwriting and drawing samples by fusing features. Applied Intelligence. 2022:1-17.
[Crossref] [Google Scholar]
[5]Aggarwal A, Varma PS, Sehgal L, Shah KP, Gupta GU, Ranjan S. Detection of personality using machine learning. International Journal of Research in Engineering, Science and Management. 2022; 5(1):46-50.
[Google Scholar]
[6]Fallah B, Khotanlou H. Identify human personality parameters based on handwriting using neural network. In artificial intelligence and robotics 2016 (pp. 120-6). IEEE.
[Crossref] [Google Scholar]
[7]Mekhaznia T, Djeddi C, Sarkar S. Personality traits identification through handwriting analysis. In Mediterranean conference on pattern recognition and artificial intelligence 2020 (pp. 155-69). Springer, Cham.
[Crossref] [Google Scholar]
[8]Mutalib S, Rahman SA, Yusoff M, Mohamed A. Personality analysis based on letter ‘t’ using back propagation neural network. In proceedings of the international conference on electrical engineering and informatics institut teknologi bandung, Indonesia. 2007 (pp. 17-9).
[Google Scholar]
[9]Gavrilescu M, Vizireanu N. Predicting the big five personality traits from handwriting. EURASIP Journal on Image and Video Processing. 2018; 2018(1):1-17.
[Crossref] [Google Scholar]
[10]Mishra PK, Abidi AI, Mishra GS. Improved methodology for personality assessment using handwritten documents. Journal of Positive School Psychology. 2022:3263-73.
[Google Scholar]
[11]Mishra A. Forensic graphology: assessment of personality. Forensic Research & Criminology International Journal. 2017; 4(1):9-12.
[Google Scholar]
[12]Asra S, Shubhangi DC. Human behavior recognition based on hand written cursives by SVM classifier. In international conference on electrical, electronics, communication, computer, and optimization techniques 2017 (pp. 260-8). IEEE.
[Crossref] [Google Scholar]
[13]Champa HN, Anandakumar KR. Artificial neural network for human behavior prediction through handwriting analysis. International Journal of Computer Applications. 2010; 2(2):36-41.
[Google Scholar]
[14]Rahiman A, Varghese D, Kumar M. Habit: handwritten analysis based individualistic traits prediction. International Journal of Image Processing. 2013; 7(2):209-18.
[Google Scholar]
[15]Fisher J, Maredia A, Nixon A, Williams N, Leet J. Identifying personality traits, and especially traits resulting in violent behavior through automatic handwriting analysis. Proceedings of Student-Faculty Research Day, CSIS, Pace University. 2012.
[Google Scholar]
[16]Prasad S, Singh VK, Sapre A. Handwriting analysis based on segmentation method for prediction of human personality using support vector machine. International Journal of Computer Applications. 2010; 8(12):25-9.
[Google Scholar]
[17]Grewal PK, Prashar D. Behavior prediction through handwriting analysis. International Journal of Computer Science and Technology. 2012; 3(2):520-3.
[Google Scholar]
[18]Coll R, Fornés A, Lladós J. Graphological analysis of handwritten text documents for human resources recruitment. In international conference on document analysis and recognition 2009 (pp. 1081-5). IEEE.
[Crossref] [Google Scholar]
[19]Mukherjee S, De I. Feature extraction from handwritten documents for personality analysis. In international conference on computer, electrical & communication engineering 2016 (pp. 1-8). IEEE.
[Crossref] [Google Scholar]
[20]Joshi P, Agarwal A, Dhavale A, Suryavanshi R, Kodolikar S. Handwriting analysis for detection of personality traits using machine learning approach. International Journal of Computer Applications. 2015; 130(15):40-5.
[Google Scholar]
[21]Kacker R, Maringanti HB. Personality analysis through handwriting. GSTF Journal on Computing. 2014; 2(1):94-7.
[Google Scholar]
[22]Mutalib S, Ramli R, Rahman SA, Yusoff M, Mohamed A. Towards emotional control recognition through handwriting using fuzzy inference. In international symposium on information technology 2008 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[23]Wijaya W, Tolle H, Utaminingrum F. Personality analysis through handwriting detection using android based mobile device. Journal of Information Technology and Computer Science. 2017; 2(2):114-28.
[Crossref] [Google Scholar]
[24]Chitlangia A, Malathi G. Handwriting analysis based on histogram of oriented gradient for predicting personality traits using SVM. Procedia Computer Science. 2019; 165:384-90.
[Crossref] [Google Scholar]
[25]Pratiwi D, Santoso GB, Saputri FH. Personality type assessment system by using enneagram-graphology techniques on digital handwriting. International Journal of Computer Applications. 2016; 147(11):9-13.
[Google Scholar]
[26]Majumder N, Poria S, Gelbukh A, Cambria E. Deep learning-based document modeling for personality detection from text. IEEE Intelligent Systems. 2017; 32(2):74-9.
[Crossref] [Google Scholar]
[27]Lokhande VR, Gawali BW. Analysis of signature for the prediction of personality traits. In 1st international conference on intelligent systems and information management 2017 (pp. 44-9). IEEE.
[Crossref] [Google Scholar]
[28]Hashemi S, Vaseghi B, Torgheh F. Graphology for Farsi handwriting using image processing techniques. IOSR Journal of Electronics and Communication Engineering. 2015; 10(3):1-7.
[Google Scholar]
[29]Chaubey G, Arjaria SK. Personality prediction through handwriting analysis using convolutional neural networks. In proceedings of international conference on computational intelligence 2022 (pp. 59-70). Springer, Singapore.
[Crossref] [Google Scholar]
[30]Rahman AU, Halim Z. Predicting the big five personality traits from hand-written text features through semi-supervised learning. Multimedia Tools and Applications. 2022;81:33671-87.
[Crossref] [Google Scholar]
[31]Anari MS, Rezaee K, Ahmadi A. TraitLWNet: a novel predictor of personality trait by analyzing Persian handwriting based on lightweight deep convolutional neural network. Multimedia Tools and Applications. 2022; 81(8):10673-93.
[Crossref] [Google Scholar]
[32]Samsuryadi RK, Mohamad FS. Automated handwriting analysis based on pattern recognition: a survey. Indonesian Journal of Electrical Engineering and Computer Science. 2021; 22(1):196-206.
[Crossref] [Google Scholar]
[33]Pathak AR, Raut A, Pawar S, Nangare M, Abbott HS, Chandak P. Personality analysis through handwriting recognition. Journal of Discrete Mathematical Sciences and Cryptography. 2020; 23(1):19-33.
[Crossref] [Google Scholar]
[34]Nolazco-Flores JA, Faundez-Zanuy M, Velázquez-Flores OA, Cordasco G, Esposito A. Emotional state recognition performance improvement on a handwriting and drawing task. IEEE Access. 2021; 9:28496-504.
[Crossref] [Google Scholar]
[35]Elngar AA, Jain N, Sharma D, Negi H, Trehan A, Srivastava A. A deep learning based analysis of the big five personality traits from handwriting samples using image processing. Journal of Information Technology Management. 2020:3-35.
[Google Scholar]
[36]Impedovo D, Pirlo G. Dynamic handwriting analysis for the assessment of neurodegenerative diseases: a pattern recognition perspective. IEEE Reviews in Biomedical Engineering. 2018; 12:209-20.
[Crossref] [Google Scholar]
[37]Gahmousse A, Gattal A, Djeddi C, Siddiqi I. Handwriting based personality identification using textural features. In international conference on data analytics for business and industry: way towards a sustainable economy 2020 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[38]Valdez-rodríguez JE, Calvo H, Felipe-riveron EM. Handwritten texts for personality identification using convolutional neural networks. In international conference on pattern recognition 2018 (pp. 140-5). Springer, Cham.
[Crossref] [Google Scholar]
[39]Lemos N, Shah K, Rade R, Shah D. Personality prediction based on handwriting using machine learning. In international conference on computational techniques, electronics and mechanical systems 2018 (pp. 110-3). IEEE.
[Crossref] [Google Scholar]
[40]Kedar S, Bormane DS. An approach to predict hypertension based on handwritten manuscript. Indian Journal of Public Health Research & Development. 2018; 9(11): 2235-40.
[Google Scholar]
[41]Fatimah SH, Djamal EC, Ilyas R, Renaldi F. Personality features identification from handwriting using convolutional neural networks. In 4th international conference on information technology, information systems and electrical engineering 2019 (pp. 119-24). IEEE.
[Crossref] [Google Scholar]
[42]Costa EP, Villaseñor-Pienda L, Morales E, Escalante HJ. Recognition of apparent personality traits from text and handwritten images. In international conference on pattern recognition 2018 (pp. 146-52). Springer, Cham.
[Crossref] [Google Scholar]
[43]https://openpsychometrics.org/tests/IPIP-BFFM/. Accessed 30 June 2022.
[44]Deepu R, Murali S, Raju V. A mathematical model for the determination of distance of an object in a 2D image. In proceedings of the international conference on image processing, computer vision, and pattern recognition 2013 (pp.1-5).
[Google Scholar]
[45]https://books.google.co.in/books/about/The_Little_Giant_Encyclopedia_of_Handwri.html?id=ADsOAAAACAAJ&redir_esc=y. Accessed 30 June 2022.
[46]https://www.nist.gov/itl/products-and-services/emnist-dataset. Accessed 30 June 2022.
[47]Weng Y, Xia C. A new deep learning-based handwritten character recognition system on mobile computing devices. Mobile Networks and Applications. 2020; 25(2):402-11.
[Crossref] [Google Scholar]