Abstract
The task consists in estimating the personality traits of users from their handwritten texts. To classify them, we use the scanned image of the subject hand-written essay divided in patches and we propose in this work an architecture based on a Convolutional Neural Network (CNN) as classifier. The original dataset consists of 418 images in color, from which we obtained 216 patches of each image in grayscale and then we binarized them resulting in approximately 90,000 images. The CNN consists of five convolutional layers to extract features of the patches and three fully connected layers to perform the classification.
We thank Instituto Politécnico Nacional (SIP, COFAA, EDI and BEIFI), and CONACyT for their support for this research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). Software available from tensorflow.org
Arora, R., Basu, A., Mianjy, P., Mukherjee, A.: Understanding deep neural networks with rectified linear units. arXiv preprint arXiv:1611.01491 (2016)
Champa, H., AnandaKumar, K.: Artificial neural network for human behavior prediction through handwriting analysis. Int. J. Comput. Appl. 0975–8887 Volume (2010)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Gonzalez, R.C., Woods, R.E., Eddins, S.L., et al.: Digital Image Processing Using MATLAB, vol. 624. Pearson-Prentice-Hall, Upper Saddle River (2004)
Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
LeCun, Y., et al.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, pp. 396–404 (1990)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
LeCun, Y.A., Bottou, L., Orr, G.B., Müller, K.-R.: Efficient BackProp. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 9–48. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35289-8_3
McCrae, R.R., John, O.P.: An introduction to the five-factor model and its applications. J. Pers. 60(2), 175–215 (1992)
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)
Ramírez-de-la Rosa, G., et al.: Overview of the multimedia information processing for personality & social networks analysis contest. In: ICPR Workshop Proceedings (2018)
Tupes, E.C., Christal, R.E.: Recurrent personality factors based on trait ratings. J. Pers. 60(2), 225–251 (1992)
Xing, L., Qiao, Y.: DeepWriter: a multi-stream deep CNN for text-independent writer identification. CoRR, abs/1606.06472 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Valdez-Rodríguez, J.E., Calvo, H., Felipe-Riverón, E.M. (2019). Handwritten Texts for Personality Identification Using Convolutional Neural Networks. 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_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-05792-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05791-6
Online ISBN: 978-3-030-05792-3
eBook Packages: Computer ScienceComputer Science (R0)