Classifying Children with 3D Depth Cameras for Enabling Children's Safety Applications

Basaran C., Yoon H. J., Ra H. K., Son S. H., Park T., Ko J.

ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), Washington, United States Of America, 13 - 17 September 2014, pp.343-347 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1145/2632048.2636074
  • City: Washington
  • Country: United States Of America
  • Page Numbers: pp.343-347
  • Keywords: Child Classification, Kinect-based Applications
  • Middle East Technical University Affiliated: No

Abstract

In this work, we present ChildSafe, a classification system which exploits human skeletal features collected using a 3D depth camera to classify visual characteristics between children and adults. ChildSafe analyzes the histograms of training samples and implements a bin-boundary-based classifier. We train and evaluate Child-Safe using a large dataset of visual samples collected from 150 elementary school children and 43 adults, ranging in the ages of 7 and 50. Our results suggest that ChildSafe successfully detects children with a proper classification rate of up to 97%, a false negative rate of as low as 1.82%, and a low false positive rate of 1.46%. We envision this work as an effective sub-system for designing various child protection applications.