Active detection of age groups based on touch interaction. Issue 1 (5th November 2018)

• Record Type: Journal Article
• Authors: Acien, Alejandro; Morales, Aythami; Fierrez, Julian; Vera‐Rodriguez, Ruben; Hernandez‐Ortega, Javier
• Is Part Of: IET biometrics. Volume 8:Issue 1(2019)
• Page Start: 101
• Page End: 108
• Publication Date: 2018-11-05
• Subjects: paediatrics -- support vector machines -- touch sensitive screens -- feature extraction -- smart phones -- signal classification; support vector machines -- age groups -- correct classification rates -- neuromotor‐inspired features -- touch devices -- touch interaction -- touchscreen devices -- sigma‐lognormal theory -- global characterisation -- touchscreen interaction -- active detection approach -- user classification; Biometric identification -- Periodicals; 570.15195
• DOI: 10.1049/iet-bmt.2018.5003
• Languages: English
• ISSNs: 2047-4938

Abstract

Abstract : 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.