Open AccessActive detection of age groups based on touch interaction
Author(s) -
Acien Alejandro,
Morales Aythami,
Fierrez Julian,
VeraRodriguez Ruben,
HernandezOrtega Javier
Publication year - 2019
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/iet-bmt.2018.5003
Subject(s) - touchscreen , gesture , discriminative model , computer science , support vector machine , artificial intelligence , set (abstract data type) , data set , pattern recognition (psychology) , age groups , machine learning , human–computer interaction , demography , sociology , programming language
This article studies user classification into children and adults according to their interaction with touchscreen devices. The authors analyse the performance of two sets of features derived from the sigma‐lognormal theory of rapid human movements and global characterisation of touchscreen interaction. The authors propose an active detection approach aimed to continuously monitor the user patterns. The experimentation is conducted on a publicly available database with samples obtained from 89 children between 3 and 6 years old and 30 adults. The authors have used support vector machines algorithm to classify the resulting features into age groups. The sets of features are fused at the score level using data from smartphones and tablets. The results, with correct classification rates over 96%, show the discriminative ability of the proposed neuromotor‐inspired features to classify age groups according to the interaction with touch devices. In the active detection set‐up, the authors’ method is able to identify a child using only four gestures in average.