Author/Authors :
Marzinotto, Gabriel University of Paris-Saclay - Palaiseau, France , Rosales, José C University of Paris-Saclay - Palaiseau, France , EL-Yacoubi, Mounîm A University of Paris-Saclay - Palaiseau, France , Garcia-Salicetti, Sonia University of Paris-Saclay - Palaiseau, France , Kahindo, Christian University of Paris-Saclay - Palaiseau, France , Kerhervé, Hélène Universite Paris Descartes - Paris, France , Cristancho-Lacroix, Victoria Universite Paris Descartes - Paris, France , Rigaud, Anne-Sophie Universite Paris Descartes - Paris, France
Abstract :
Characterizing age from handwriting (HW) has important applications, as it is key to distinguishing normal HW evolution with age
from abnormal HW change, potentially triggered by neurodegenerative decline. We propose, in this work, an original approach for
online HW style characterization based on a two-level clustering scheme. The first level generates writer-independent word clusters
from raw spatial-dynamic HW information. At the second level, each writer’s words are converted into a Bag of Prototype Words
that is augmented by an interword stability measure. This two-level HW style representation is input to an unsupervised learning
technique, aiming at uncovering HW style categories and their correlation with age. To assess the effectiveness of our approach, we
propose information theoretic measures to quantify the gain on age information from each clustering layer. We have carried out
extensive experiments on a large public online HW database, augmented by HW samples acquired at Broca Hospital in Paris from
people mostly between 60 and 85 years old. Unlike previous works claiming that there is only one pattern of HW change with age,
our study reveals three major aging HW styles, one specific to aged people and the two others shared by other age groups.
