<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christian Lang</style></author><author><style face="normal" font="default" size="100%">Wachsmuth, Sven</style></author><author><style face="normal" font="default" size="100%">Hanheide, Marc</style></author><author><style face="normal" font="default" size="100%">Heiko Wersing</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Facial Communicative Signal Interpretation in Human-Robot Interaction by Discriminative Video Subsequence Selection</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CoR-Lab Publication</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2012</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Bielefeld University, Faculty of Technology, Research Institute for Cognition and Robotics / Applied Informatics</style></publisher><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Facial communicative signals (FCSs) such as head gestures, eye gaze, and facial expressions can provide useful feedback in conversations between people and also in human-robot interaction. This paper presents a pattern recognition approach for the interpretation of FCSs in terms of valence, based on the selection of discriminative subsequences in video data. These subsequences capture important temporal dynamics and are used as prototypical reference subsequences in a classification procedure based on dynamic time warping and feature extraction with active appearance models. The approach is evaluated on a database containing videos of people interacting with a robot by teaching the names of several objects to it. The verbal answer of the robot is expected to elicit the display of spontaneous FCSs by the human tutor, which were classified in this work. The achieved classification rates are comparable to the average human recognition performance and outperformed our previous results on this task.&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Technical Report</style></work-type></record></records></xml>
