Abstract
This work describes how a new multimodal data base is constructed to try to solve the link between signature/handwriting and personality. With the help of two devices, one of them responsible to report the mechanical writing process (using a tablet) and the other one to acquire brain activity (with an EEG headset) we will be able to carry out different sessions through a set of experiments. Because the data base is not completed yet, and it is well known that deep learning requires larges amount of data, the main results about signature and personality factors were not good enough. The different deep convolutional neural networks (DCNN) tested does not obtain a reasonable minimum threshold. However the same incomplete data base gives promising results when solving a completely different problem such as signature recognition (where a performance of 80% was reached) using the same DCNN architecture.
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References
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, Nov 2013
Komarraju, M., Karau, S.J., Schmeck, R.R., Avdic, A.: The Big Five personality traits, learning styles, and academic achievement. Personal. Individ. Differ. 51, 472–477 (2011)
Schmidhuber, J.: Deep Learning in Neural Networks: An Overview, Apr 2014
Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E.: Deep learning for computer vision: a brief review. Comput. Intell. Neurosci. 2018, 1–13 (2018)
Pascual-Gaspar, J.M., Faundez-Zanuy, M., Vivaracho, C.: Fast on-line signature recognition based on VQ with time modeling. Eng. Appl. Artif. Intell. 24, 368–377 (2011)
Faundez-Zanuy, M., Pascual-Gaspar, J.M.: Efficient on-line signature recognition based on multi-section vector quantization. Pattern Anal. Appl. 14, 37–45 (2011)
Sesa-Nogueras, E., Faundez-Zanuy, M., Roure-Alcobé, J.: Gender classification by means of online uppercase handwriting: a text-dependent allographic approach. Cogn. Comput. 8, 15–29 (2016)
Faundez-Zanuy, M., Sesa-Nogueras, E.: Preliminary Experiments on Automatic Gender Recognition Based on Online Capital Letters, pp. 363–370 (2014)
Faundez-Zanuy, M., Fierrez-Aguilar, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J.: Multimodal biometric databases: an overview. IEEE Aerosp. Electron. Syst. Mag. 21(8), 29–37 (2006)
Fierrez, J., Galbally, J., Ortega-Garcia, J., Freire, M.R., Alonso-Fernandez, F., Ramos, D., Toledano, D.T., Gonzalez-Rodriguez, J., Siguenza, J.A., Garrido-Salas, J., Anguiano, E., Gonzalez-de Rivera, G., Ribalda, R., Faundez-Zanuy, M., Ortega, J.A., Cardeñoso-Payo, V., Viloria, A., Vivaracho, C.E., Moro, Q.I., Igarza, J.J., Sanchez, J., Hernaez, I., Orrite-Uruñuela, C., Martinez-Contreras, F., Gracia-Roche, J.J.: BiosecurID: a multimodal biometric database. Pattern Anal. Appl. 13, 235–246 (2010)
Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Espinosa, V., Satue, A., Hernaez, I., Igarza, J.-J., Vivaracho, C., Escudero, D., Moro, Q.-I.: MCYT baseline corpus: a bimodal biometric database. IEE Proc. Vis. Image Signal Process. 150(6), 395 (2003)
Cobb-Clark, D.A., Schurer, S.: The stability of big-five personality traits. Econ. Lett. 115, 11–15 (2012)
Furnham, A., Cheng, H.: The Big-Five personality factors, mental health, and social-demographic indicators as independent predictors of gratification delay. Personal. Individ. Differ. 150, 109533 (2019)
Stanzani Maserati, M., Matacena, C., Sambati, L., Oppi, F., Poda, R., De Matteis, M., Gallassi, R.: The tree-drawing test (Koch’s Baum test): a useful aid to diagnose cognitive impairment. Behav. Neurol. 2015, 1–6 (2015)
Chi, Y.M., Wang, Y., Wang, Y.-T., Jung, T.-P., Kerth, T., Cao, Y.: A Practical Mobile Dry EEG System for Human Computer Interfaces, pp. 649–655 (2013)
Lecun, Y.: Generalization and network design strategies. Technical report, University of Toronto (1989)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Chollet, F.: Xception: Deep Learning with Depthwise Separable Convolutions, Oct 2016
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017)
Perez, L., Wang, J.: The Effectiveness of Data Augmentation in Image Classification Using Deep Learning, Dec 2017
Shima, Y.: Image augmentation for object image classification based on combination of pre-trained CNN and SVM. J. Phys. Conf. Ser. 1004, 012001 (2018)
Salakhutdinov, N.S., Hinton, G., Krizhevsky, A., Sutskever, I., Ruslan, S.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Han, K., Sethi, I.K.: Handwritten signature retrieval and identification. Pattern Recognit. Lett. 17, 83–90 (1996)
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This work has been supported by FEDER and MEC, TEC2016-77791-C4-2-R and PID2019-109099RB-C42.
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Font, X., Delgado, A., Faundez-Zanuy, M. (2021). Preliminary Study on the Behavioral Traits Obtained from Signatures and Writing Using Deep Learning Algorithms. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_19
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