Abstract
Age characterizationfrom handwriting (HW) has important applications as it may allow distinguishing normal HW evolution due to age from abnormal HW change, potentially related to a cognitive decline. We propose, in this work, an original approach for online HW style characterization based on a two-level clustering scheme. The first level allows generating writer-independent word clusters according to raw spatial-dynamic HW information. At the second level, the writer words are converted into a Bag of Prototype Words that is augmented by a measure of his/her writing stability across words. For age characterization, we harness the two-level HW style representation using unsupervised and supervised schemes, the former aiming at uncovering HW style categories and their correlation with age and the latter at predicting age groups. Our experiments on a large database show that the two level representation uncovers interesting correlations between age and HW style. The evaluation is based on entropy-based information theoretic measures to quantify the gain on age information from the proposed two-level HW style representation.
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References
Vuori, V.: Clustering writing styles with a self-organizing map. In: IWFHR 2002 (2002)
Sarkar, P., Nagy, G.: Style consistent classification of isogenous patterns. IEEE PAMI 27(1), 88–98 (2005)
Teulings, H.-L., et al.: Adaptation of handwriting size under distorted visual feedback in patients with Parkinson’s disease and elderly and young controls. J. Neurol. Neurosurg. Psychiatry 72, 315–324 (2002)
Bharath, A., Deepu, V., Madhvanath, S.: An approach to identify unique styles in online handwriting recognition. In: ICDAR 2005 (2005)
Chan,S.K., Tay, Y.H., Viard-Gaudin, C.: Online text independent writer identification using character prototypes distribution. In: SPIE Electronic Imaging 2008 (2008)
Crettez, J.-P.: A set of handwriting families: style recognition. In: ICDAR 1995 (1995)
Viard-Gaudin, C., et al.: The IRONOFF dual handwriting database. In: ICDAR 1999 (1999)
Guyon, I., et al.: Design of a neural network character recognizer for a touch terminal. Pattern Recogn. 24, 105–119 (1991)
Hinton, G., Roweis, S.: Stochastic neighbor embedding. NIPS 15, 833–840 (2002)
Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Calinski, R.B., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theor. Methods 41, 2279–2280 (2012)
Rosenblum, S., et al.: Age-related changes in executive control and their relationships with activity performance in handwriting. Hum. Mov. Sci. 32, 1056–1069 (2013)
Sivic, J.: Efficient visual search of videos cast as text retrieval. IEEE Trans. PAMI 31(4), 591–605 (2009)
Acknowledgments
This work was partially funded by Fondation MAIF through project “Biométrie et santé sur tablette”. For more information, please refer to: http://www.fondation-maif.fr/notre-action.php?rub=1&sous_rub=3&id=269.
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Rosales, J.C., Marzinotto, G., El-Yacoubi, M.A., Garcia-Salicetti, S. (2016). Age Characterization from Online Handwriting. In: Serino, S., Matic, A., Giakoumis, D., Lopez, G., Cipresso, P. (eds) Pervasive Computing Paradigms for Mental Health. MindCare 2015. Communications in Computer and Information Science, vol 604. Springer, Cham. https://doi.org/10.1007/978-3-319-32270-4_18
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DOI: https://doi.org/10.1007/978-3-319-32270-4_18
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