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Personality Traits Identification Through Handwriting Analysis

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Pattern Recognition and Artificial Intelligence (MedPRAI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1322))

Abstract

Personality traits are of paramount importance in identifying the human’s behavior. They represent a useful information source for forensic control, recruitment profiling, medical symptoms, and other applications. Personality traits are identified through various physical aspects, including sense, honesty, and other emotions. These aspects can be revealed through handwritten features. Since handwriting is unique for everyone, its identification process is not as straight forward as it appears; rather it involves efficient tools for extraction and classification of features. The process has been the subject of various research works. However, results reported remain unsatisfactory due to mainly dissimilarities in handwriting. In this paper, we present an approach of recognition of personality traits based on textural features extracted from handwritten samples. Experiments are carried out using artificial neural networks and the TxPI-u database. Results deliver a significant recognition rate which endorses its effectiveness against similar works.

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References

  1. Desbarrolles, A., Jean-Hippolyte: Les mystères de l’ecriture, 2eme edition. Paris (1872)

    Google Scholar 

  2. Lauer, F., Suen, C.Y., Bloch, G.: A trainable feature extractor for handwritten digit recognition. Pattern Recogn. (2007). https://doi.org/10.1016/j.patcog.2006.10.011

  3. Sheikholeslami, G.S.N.V.: Computer aided graphology (1997)

    Google Scholar 

  4. LeCun, Y., Cortes, C., Burges, C.: The MNIST database of handwritten digits. Courant Institute of Mathematical Sciences (1998)

    Google Scholar 

  5. Zois, E.N., Anastassopoulos, V.: Morphological waveform coding for writer identification. Pattern Recogn. (2000). https://doi.org/10.1016/S0031-3203(99)00063-1

  6. Cha, S.-H., Srihari, S.N.: Writer identification: statistical analysis and dichotomizer. In: Ferri, F.J., Iñesta, J.M., Amin, A., Pudil, P. (eds.) SSPR /SPR 2000. LNCS, vol. 1876, pp. 123–132. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44522-6_13

    Chapter  Google Scholar 

  7. Kamath, V., Ramaswamy, N., Navin Karanth, P., Desai, V., Kulkarni, S.M.: Development of an automated handwriting analysis system. J. Eng. Appl. Sci. 6 (2011)

    Google Scholar 

  8. Champa, H.N., AnandaKumar, K.R.: Automated human behavior prediction through handwriting analysis. In: Proceedings - 1st International Conference on Integrated Intelligent Computing, ICIIC 2010 (2010). https://doi.org/10.1109/ICIIC.2010.29

  9. Grewal, P.K., Prashar, D.: Behavior prediction through handwriting analysis. In: IJCST (2012)

    Google Scholar 

  10. Mukherjee, S., De, I.: Feature extraction from handwritten documents for personality analysis. In: 2016 International Conference on Computer, Electrical and Communication Engineering, ICCECE 2016 (2017). https://doi.org/10.1109/ICCECE.2016.8009580

  11. Zafar, M.F., Mohamad, D., Anwar, M.M.: Recognition of online isolated handwritten characters by backpropagation neural nets using sub-character primitive features. In: 10th IEEE International Multitopic Conference, INMIC 2006 (2006). https://doi.org/10.1109/INMIC.2006.358154

  12. Cha, S.H., Tappert, C.C.: Automatic detection of handwriting forgery. In: Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR (2002). https://doi.org/10.1109/IWFHR.2002.1030920

  13. Said, H.E.S., Baker, K.D., Tan, T.N.: Personal identification based on handwriting (2002). https://doi.org/10.1109/icpr.1998.712068

  14. Mogharreban, N., Rahimi, S., Sabharwal, M.: A combined crisp and fuzzy approach for handwriting analysis. In: Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS (2004). https://doi.org/10.1109/nafips.2004.1336307

  15. Mutalib, S., Ramli, R., Rahman, S.A., Yusoff, M., Mohamed, A.: Towards emotional control recognition through handwriting using fuzzy inference. In: Proceedings - International Symposium on Information Technology, ITSim (2008). https://doi.org/10.1109/ITSIM.2008.4631735

  16. Abdul Rahiman, M., Varghese, D., Kumar, M.: HABIT: handwritten analysis based individualistic traits prediction (2013)

    Google Scholar 

  17. Joshi, P., Agarwal, A., Dhavale, A., Suryavanshi, R., Kodolikar, S.: Handwriting analysis for detection of personality traits using machine learning approach. Int. J. Comput. Appl. (2015). https://doi.org/10.5120/ijca2015907189

    Article  Google Scholar 

  18. Niu, X.X., Suen, C.Y.: A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recogn. (2012). https://doi.org/10.1016/j.patcog.2011.09.021

  19. Chaudhari, K., Thakkar, A.: Survey on handwriting-based personality trait identification. Expert Syst. Appl. (2019). https://doi.org/10.1016/j.eswa.2019.01.028

  20. Ramírez-De-La-Rosa, G., Villatoro-Tello, E., Jiménez-Salazar, H.: TxPI-u: a resource for Personality Identification of undergraduates. J. Intell. Fuzzy Syst. (2018). https://doi.org/10.3233/JIFS-169484

    Article  Google Scholar 

  21. Blostein, D., Baird, H.S.: A critical survey of music image analysis. In: Structured Document Image Analysis (2011)

    Google Scholar 

  22. Dutta, A., Pal, U., Fornés, A., Lladós, J.: An efficient staff removal approach from printed musical documents. In: Proceedings - International Conference on Pattern Recognition (2010). https://doi.org/10.1109/ICPR.2010.484

  23. Pinto, T., Rebelo, A., Giraldi, G., Cardoso, J.S.: Music score binarization based on domain knowledge. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 700–708. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21257-4_87

    Chapter  Google Scholar 

  24. Phangtriastu, M.R., Harefa, J., Tanoto, D.F.: Comparison between neural network and support vector machine in optical character recognition. In: Procedia Computer Science (2017). https://doi.org/10.1016/j.procs.2017.10.061

  25. dos Santos Cardoso, J., Capela, A., Rebelo, A., Guedes, C., Pinto da Costa, J.: Staff detection with stable paths. IEEE Trans. Pattern Anal. Mach. Intell. (2009). https://doi.org/10.1109/TPAMI.2009.34

  26. Bulacu, M., Schomaker, L., Vuurpijl, L.: Writer identification using edge-based directional features. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR (2003). https://doi.org/10.1109/ICDAR.2003.1227797

  27. Arazi, B.: Handwriting identification by means of run-length measurements. IEEE Trans. Syst. Man Cybern. (1977). https://doi.org/10.1109/TSMC.1977.4309648

    Article  Google Scholar 

  28. Köppel, M., Doshkov, D., Racape, F., Ndjiki-Nya, P., Wiegand, T.: On the usage of the 2D-AR-model in texture completion scenarios with causal boundary conditions: a tutorial. Signal Process Image Commun. (2015). https://doi.org/10.1016/j.image.2015.01.010

    Article  Google Scholar 

  29. de Souza, P.: Texture recognition via auto regression. Pattern Recogn. (1982). https://doi.org/10.1016/0031-3203(82)90025-5

  30. Bulacu, M., Schomaker, L.: Writer style from oriented edge fragments. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 460–469. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45179-2_57

    Chapter  Google Scholar 

  31. Crettea, J.P.: A set of handwriting families: style recognition. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR (1995). https://doi.org/10.1109/ICDAR.1995.599041

  32. Li, H., Toh, K.A., Li, L.: Advanced Topics in Biometrics. World Scientific, Singapore (2011)

    Google Scholar 

  33. Sarkhel, R., Das, N., Das, A., Kundu, M., Nasipuri, M.: A multi-scale deep quad tree based feature extraction method for the recognition of isolated handwritten characters of popular Indic scripts. Pattern Recogn. (2017) https://doi.org/10.1016/j.patcog.2017.05.022

  34. Breiman, L.: Random forests. Mach. Learn. (2001). https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  35. Pramanik, R., Raj, V., Bag, S.: Finding the optimum classifier: classification of segmentable components in offline handwritten Devanagari words. In: Proceedings of the 4th IEEE International Conference on Recent Advances in Information Technology, RAIT 2018 (2018). https://doi.org/10.1109/RAIT.2018.8389032

  36. Elleuch, M., Zouari, R., Kherallah, M.: Feature extractor based deep method to enhance online Arabic handwritten recognition system. In: Villa, A.E.P., Masulli, P., Pons Rivero, A.J. (eds.) ICANN 2016. LNCS, vol. 9887, pp. 136–144. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44781-0_17

    Chapter  Google Scholar 

  37. David, G.S., Duda, R.O., Hart, P.E.: Pattern classification, 2nd edn. Wiley, Hoboken (2012)

    Google Scholar 

  38. Duval, F.: Artificial Neural Networks: Concepts, Tools and Techniques Explained for Absolute Beginners. 2018

    Google Scholar 

  39. Al Maadeed, S., Hassaine, A.: Automatic prediction of age, gender, and nationality in offline handwriting. EURASIP J. Image Video Process. 2014(1), 1–10 (2014). https://doi.org/10.1186/1687-5281-2014-10

    Article  Google Scholar 

  40. Majumdar, A., Krishnan, P., Jawahar, C.V.: Visual aesthetic analysis for handwritten document images. In: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR (2016). https://doi.org/10.1109/ICFHR.2016.0085

  41. Chen, Z., Lin, T.: Automatic personality identification using writing behaviours: an exploratory study. Behav. Inf. Technol. (2017). https://doi.org/10.1080/0144929X.2017.1304994

    Article  Google Scholar 

  42. Mutalib, S., Rahman, S.A., 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 (2007)

    Google Scholar 

  43. Gavrilescu, M.: Study on determining the Myers-Briggs personality type based on individual’s handwriting. In: 2015 E-Health and Bioengineering Conference, EHB 2015 (2016). https://doi.org/10.1109/EHB.2015.7391603

  44. Asra, S., Shubhangi, D.C.: Human behavior recognition based on hand written cursives by SVM classifier. In: International Conference on Electrical, Electronics, Communication Computer Technologies and Optimization Techniques, ICEECCOT 2017 (2018). https://doi.org/10.1109/ICEECCOT.2017.8284679

  45. Wijaya, W., Tolle, H., Utaminingrum, F.: Personality analysis through handwriting detection using android based mobile device. J. Inf. Technol. Comput. Sci. (2018) https://doi.org/10.25126/jitecs.20172237

  46. Arica, N., Yarman-Vural, F.T.: An overview of character recognition focused on off-line handwriting. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. (2001). https://doi.org/10.1109/5326.941845

    Article  Google Scholar 

  47. Fallah, B., Khotanlou, H.: Detecting features of human personality based on handwriting using learning algorithms Adv. Comput. Sci. Int. J. 4, 31–37 (2015)

    Google Scholar 

  48. Sen, A., Shah, H.: Automated handwriting analysis system using principles of graphology and image processing. In: Proceedings of 2017 International Conference on Innovations in Information, Embedded and Communication Systems, ICIIECS 2017 (2018). https://doi.org/10.1109/ICIIECS.2017.8276061

  49. Bal, A., Saha, R.: An improved method for handwritten document analysis using segmentation, baseline recognition and writing pressure detection. Procedia Comput. Sci. (2016). https://doi.org/10.1016/j.procs.2016.07.227

  50. Prasad, S., Singh, V.K., Sapre, A.: Handwriting analysis based on segmentation method for prediction of human personality using support vector machine. Int. J. Comput. Appl. (2010). https://doi.org/10.5120/1256-1758

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Tebessa, Tebessa, Algeria

    Tahar Mekhaznia & Chawki Djeddi

  2. University of Edinburgh, Edinburgh, UK

    Sobhan Sarkar

Authors
  1. Tahar Mekhaznia
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  2. Chawki Djeddi
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  3. Sobhan Sarkar
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Corresponding author

Correspondence to Tahar Mekhaznia .

Editor information

Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Chawki Djeddi

  2. Digital Research Center of Sfax, Sfax, Tunisia

    Yousri Kessentini

  3. Bahria University, Islamabad, Pakistan

    Imran Siddiqi

  4. Digital Research Centre of Sfax, Sfax, Tunisia

    Mohamed Jmaiel

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Mekhaznia, T., Djeddi, C., Sarkar, S. (2021). Personality Traits Identification Through Handwriting Analysis. In: Djeddi, C., Kessentini, Y., Siddiqi, I., Jmaiel, M. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2020. Communications in Computer and Information Science, vol 1322. Springer, Cham. https://doi.org/10.1007/978-3-030-71804-6_12

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  • DOI: https://doi.org/10.1007/978-3-030-71804-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71803-9

  • Online ISBN: 978-3-030-71804-6

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