Search

This article proposes a novel children–computer interaction (CCI) approach for the task of age group detection. This approach focuses on the automatic analysis of the time series generated from the interaction of the children with mobile devices. In particular, we extract a set of 25 time series related to spatial, pressure, and kinematic information of the children interaction while colouring a tree through a pen stylus tablet, a specific test from the large-scale public ChildCIdb database. A complete analysis of the proposed approach is carried out using different time series selection techniques to choose the most discriminative ones for the age group detection task: (i) a statistical analysis and (ii) an automatic algorithm called sequential forward search (SFS). In addition, different classification algorithms such as dynamic time warping barycenter averaging (DBA) and hidden Markov models (HMM) are studied. Accuracy results over 85% are achieved, outperforming previous approaches in the literature and in more challenging age group conditions. Finally, the approach presented in this study can benefit many children-related applications, for example, towards an age-appropriate environment with the technology.

On-line signature recognition is an area of growing interest in recent years due to the massive deployment of high-quality digitising tablets, smartphones, and tablets in many commercial sectors such as banking. In addition, handwritten signature is one of the most socially accepted biometric traits as it has been used in financial and legal agreements for over a century. In this current environment for signature biometrics, the number of stored samples or templates per user can grow very fast, making it possible to train more robust statistical user models, improving the performance of the biometric systems and in particular reducing the template ageing effect. This study carries out an exhaustive experimental analysis of template update strategies for three well-known on-line signature verification approaches, extracts various practical findings related to the template ageing effect in signature biometrics, and configures time-adaptive improved versions of the considered baseline approaches overcoming to some extent the template ageing. The proposed improved approach achieves system performances of 2.1 and 0.2% equal error rate for skilled and random forgery cases, respectively. These results show the efficacy of the proposed methodology.