Advertisement

Big-Five Personality Traits Based on Four Main Methods

  • P. HimaEmail author
  • M. ShanmugamEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)

Abstract

A cognitive structure focused to explain diverse behaviors of human regarding fixed and quantifiable features is called personality. Personality shows effects on various customs, traditions and daily routines. Big-five personality traits which is five-factor method contains agreeableness, extraversion, conscientiousness, openness, and neuroticism. This paper is a study of personality traits of humans based on usage of mobile apps, social media, handwriting analysis and facial expressions. By using this four methods people can be categorized into these five personality traits. This personality traits categorization will give best results in the areas of medical and marketing.

Keywords

Personality traits Mobile apps Social media Facial expressions Handwriting 

References

  1. 1.
    Yogish, D., Manjunath, T.N., Hegadi, R.S.: Survey on trends and methods of an intelligent answering system. In: 2017 International Conference on Electrical Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp. 346–353. IEEE (2017)Google Scholar
  2. 2.
    Laleh, A., Shahram, R.: Analyzing Facebook activities for personality recognition. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, Mexico, pp. 960–964. IEEE (2017)Google Scholar
  3. 3.
    Pramodh, K.C., Vijayalata, Y.: Automatic personality recognition of authors using big five factor model. In: IEEE International Conference on Advances in Computer Applications (ICACA), pp. 32–37. IEEE (2016)Google Scholar
  4. 4.
    Xu, R., Frey, R.M., Fleisch, E., Ilic, A.: Understanding the impact of personality traits on mobile app adoption-Insights from a large-scale field study. Comput. Hum. Behav. 62, 244–256 (2016)CrossRefGoogle Scholar
  5. 5.
    Gavrilescu, M.: Study on determining the Big-Five personality traits of an individual based on facial expressions. In: E-Health and Bioengineering Conference (EHB), pp. 1–6. IEEE (2015)Google Scholar
  6. 6.
    Abadi, M.K., Correa, J.A.M., Wache, J., Yang, H., Patras, I., Sebe, N.: Inference of personality traits and affect schedule by analysis of spontaneous reactions to affective videos. In: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 1, pp. 1–8. IEEE (2015)Google Scholar
  7. 7.
    Achana, R.A., Hegadi, R.S., Manjunath, T.N.: A novel data security framework using E-MOD for big data. In: 2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 546–551. IEEE (2015)Google Scholar
  8. 8.
    Markovikj, D., Gievska, S., Kosinski, M., Stillwell, D.: Mining Facebook data for predictive personality modeling. In: Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (ICWSM 2013), Boston, MA, USA, pp. 23–26 (2013)Google Scholar
  9. 9.
    Djamal, E.C., Darmawati, R., Ramdlan, S.N.: Application image processing to predict personality based on structure of handwriting and signature. In: 2013 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), pp. 163–168. IEEE (2013)Google Scholar
  10. 10.
    Chittaranjan, G., Jan, B., Gatica-Perez, D.: Who’s who with big-five: analyzing and classifying personality traits with smartphones. In: 2011 15th Annual International Symposium on Wearable Computers (ISWC), pp. 29–36. IEEE (2011)Google Scholar
  11. 11.
    Zhan, Y.Z., Cheng, K.Y., Chen, Y.B., Wen, C.J.: A new classifier for facial expression recognition: fuzzy buried Markov model. J. Comput. Sci. Technol. 25(3), 641–650 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    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. 8(12), 25–29 (2010)Google Scholar
  13. 13.
    Champa, H.N., AnandaKumar, K.R.: Automated human behavior prediction through handwriting analysis. In: 2010 First International Conference on Integrated Intelligent Computing (ICIIC), pp. 160–165. IEEE (2010)Google Scholar
  14. 14.
    Valstar, M., Pantic, M.: Fully automatic facial action unit detection and temporal analysis. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2006, p. 149. IEEE (2006)Google Scholar
  15. 15.
    Mogharreban, N., Rahimi, S., Sabharwal, M.: A combined crisp and fuzzy approach for handwriting analysis. In: IEEE Annual Meeting of the Fuzzy Information, Processing NAFIPS 2004, vol. 1, pp. 351–356. IEEE (2004)Google Scholar
  16. 16.
    Manjunath, T.N., Hegadi, R.S.: Data quality assessment model for data migration business enterprise. Int. J. Eng. Technol. (IJET) 5(1), 101–109 (2013)Google Scholar
  17. 17.
    Madhvanath, S., Govindaraju, V.: The role of holistic paradigms in handwritten word recognition. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 149–164 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Vignan’s Foundation for Science, Technology and ResearchGunturIndia

Personalised recommendations