Abstract
Personality has been considered as one of the most difficult human attributes to understand. It is very important as it can be used to define the uniqueness of a person, and it has a direct impact into several aspects of everyone’s life. This paper describes our participation in the HWxPI challenge @ ICPR 2018, an academic competition focusing on the development of methods for estimation of apparent personality from handwritten and textual information. The proposed solution combined information extracted from both text and images. From the textual modality, words, and other linguistic features were considered; whereas handwritten information was represented with shape features extracted from segmented characters. Although the task was extremely hard, we show that the considered features indeed convey useful information that can be used to estimate apparent personality.
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References
Cohen, G., Afshar, S., Tapson, J., van Schaik, A.: EMNIST: an extension of MNIST to handwritten letters. arXiv preprint arXiv:1702.05373 (2017)
De Campos, T., Bodla, R.B., Varma, M.: The chars74k dataset (2009)
Escalante, H.J., et al.: Explaining first impressions: modeling, recognizing, and explaining apparent personality from videos. CoRR, abs/1802.00745 (2018)
Jacques, C.S., et al.: First impressions: a survey on computer vision-based apparent personality trait analysis. CoRR, abs/1804.08046 (2018)
Matthews, G., Deary, J., Whiteman, C.: Personality Traits. Cambridge University Press, Cambridge (2009)
Pedregosa, F., et al.: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pennebaker, J.W., King, L.A.: Linguistic styles: language use as an individual difference. J. Pers. Soc. Psychol. 77(6), 1296–1312 (1999)
Ramirez, G., et al.: Overview of the multimedia information processing for personality & social networks analysis contest. In: Proceedings of the MIPPSNA Workshop (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 6, 1137–1149 (2017)
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Costa, E.P., Villaseñor-Pienda, L., Morales, E., Escalante, H.J. (2019). Recognition of Apparent Personality Traits from Text and Handwritten Images. In: Zhang, Z., Suter, D., Tian, Y., Branzan Albu, A., Sidère, N., Jair Escalante, H. (eds) Pattern Recognition and Information Forensics. ICPR 2018. Lecture Notes in Computer Science(), vol 11188. Springer, Cham. https://doi.org/10.1007/978-3-030-05792-3_14
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DOI: https://doi.org/10.1007/978-3-030-05792-3_14
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