Éditeur(s) : HAL CCSD
Résumé : International audience
The Sigma-Lognormal model of the Kinematic Theory of rapid human movements allows us to represent on-line signatures with an analytical neuromuscular model. It has been successfully used in the past to generate synthetic signatures in order to improve the performance of an automatic verification system. In this paper, we attempt for the first time to build a verification system based on the model parameters themselves. For describing individual lognormal strokes, we propose eighteen features which capture cognitive psychomotor characteristics of the signer. They are matched by means of dynamic time warping to derive a dissimilarity measure for signature verification. Promising initial results are reported for an experimental evaluation on the SUSIG visual sub-corpus, which contains some of the most skilled forgeries currently available for research.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165871
https://hal.univ-antilles.fr/hal-01165871
https://hal.univ-antilles.fr/hal-01165871/document
https://hal.univ-antilles.fr/hal-01165871/file/IGS_2015_submission_11.pdf

Éditeur(s) : HAL CCSD Frontiers
Résumé : International audience
The generation of handwriting is a complex neuromotor skill requiring the interaction of many cognitive processes. It aims at producing a message to be imprinted as an ink trace left on a writing medium. The generated trajectory of the pen tip is made up of strokes superimposed over time. The Kinematic Theory of rapid human movements and its family of lognormal models provide analytical representations of these strokes, often considered as the basic unit of handwriting. This paradigm has not only been experimentally confirmed in numerous predictive and physiologically significant tests but it has also been shown to be the ideal mathematical description for the impulse response of a neuromuscular system. This latter demonstration suggests that the lognormality of the velocity patterns can be interpreted as reflecting the behavior of subjects who are in perfect control of their movements. To illustrate this interpretation, we present a short overview of the main concepts behind the Kinematic Theory and briefly describe how its models can be exploited, using various software tools, to investigate these ideal lognormal behaviors. We emphasize that the parameters extracted during various tasks can be used to analyze some underlying processes associated with their realization. To investigate the operational convergence hypothesis, we report on two original studies. First, we focus on the early steps of the motor learning process as seen as a converging behavior toward the production of more precise lognormal patterns as young children practicing handwriting start to become more fluent writers. Second, we illustrate how aging affects handwriting by pointing out the increasing departure from the ideal lognormal behavior as the control of the fine motricity begins to decline. Overall, the paper highlights this developmental process of merging toward a lognormal behavior with learning, mastering this behavior to succeed in performing a given task, and then gradually deviating from it with aging.
ISSN: 1664-1078

hal-01098124
https://hal.archives-ouvertes.fr/hal-01098124
DOI : 10.3389/fpsyg.2013.0094

Éditeur(s) : HAL CCSD
Résumé : Best paper for innovative research
International audience
This paper investigates the interest of using the Kinematic Theory of rapid human movements to analyseand evaluate the handwriting produced by young kindergarten children in typical classroom environment. A total of66 children participated in this trial. For the preliminary results reported herein, movements from 15 children, takenevenly from three different levels (3, 4, and 5 years old), were analyzed. Our results confirm that 1) the sigmalognormalequation can model accurately childen’s movement and 2) that this modeling can differentiate betweenchildren of different school levels, hence supporting the relevance of pursuing along this original pathway.
Recent Progress in Graphonomics: Learn from the Past. IGS 2013
Nara, Japan

hal-01098125
https://hal.archives-ouvertes.fr/hal-01098125