Rawal, Niyati ; Koert, Dorothea ; Turan, Cigdem ; Kersting, Kristian ; Peters, Jan ; Stock-Homburg, Ruth (2022)
ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition.
In: Frontiers in Robotics and AI, 2022, 8
doi: 10.26083/tuprints-00020336
Artikel, Zweitveröffentlichung, Verlagsversion
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (Abstract)
The ability of a robot to generate appropriate facial expressions is a key aspect of perceived sociability in human-robot interaction. Yet many existing approaches rely on the use of a set of fixed, preprogrammed joint configurations for expression generation. Automating this process provides potential advantages to scale better to different robot types and various expressions. To this end, we introduce ExGenNet, a novel deep generative approach for facial expressions on humanoid robots. ExGenNets connect a generator network to reconstruct simplified facial images from robot joint configurations with a classifier network for state-of-the-art facial expression recognition. The robots’ joint configurations are optimized for various expressions by backpropagating the loss between the predicted expression and intended expression through the classification network and the generator network. To improve the transfer between human training images and images of different robots, we propose to use extracted features in the classifier as well as in the generator network. Unlike most studies on facial expression generation, ExGenNets can produce multiple configurations for each facial expression and be transferred between robots. Experimental evaluations on two robots with highly human-like faces, Alfie (Furhat Robot) and the android robot Elenoide, show that ExGenNet can successfully generate sets of joint configurations for predefined facial expressions on both robots. This ability of ExGenNet to generate realistic facial expressions was further validated in a pilot study where the majority of human subjects could accurately recognize most of the generated facial expressions on both the robots.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Rawal, Niyati ; Koert, Dorothea ; Turan, Cigdem ; Kersting, Kristian ; Peters, Jan ; Stock-Homburg, Ruth |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | Frontiers Media S.A. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Frontiers in Robotics and AI |
| Jahrgang/Volume einer Zeitschrift: | 8 |
| Kollation: | 11 Seiten |
| DOI: | 10.26083/tuprints-00020336 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/20336 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | The ability of a robot to generate appropriate facial expressions is a key aspect of perceived sociability in human-robot interaction. Yet many existing approaches rely on the use of a set of fixed, preprogrammed joint configurations for expression generation. Automating this process provides potential advantages to scale better to different robot types and various expressions. To this end, we introduce ExGenNet, a novel deep generative approach for facial expressions on humanoid robots. ExGenNets connect a generator network to reconstruct simplified facial images from robot joint configurations with a classifier network for state-of-the-art facial expression recognition. The robots’ joint configurations are optimized for various expressions by backpropagating the loss between the predicted expression and intended expression through the classification network and the generator network. To improve the transfer between human training images and images of different robots, we propose to use extracted features in the classifier as well as in the generator network. Unlike most studies on facial expression generation, ExGenNets can produce multiple configurations for each facial expression and be transferred between robots. Experimental evaluations on two robots with highly human-like faces, Alfie (Furhat Robot) and the android robot Elenoide, show that ExGenNet can successfully generate sets of joint configurations for predefined facial expressions on both robots. This ability of ExGenNet to generate realistic facial expressions was further validated in a pilot study where the majority of human subjects could accurately recognize most of the generated facial expressions on both the robots. |
| Freie Schlagworte: | facial expression generation, humanoid robots, facial expression recognition, neural networks, gradient descent |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-203368 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 01 Fachbereich Rechts- und Wirtschaftswissenschaften 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete > Fachgebiet Marketing & Personalmanagement 20 Fachbereich Informatik 20 Fachbereich Informatik > Intelligente Autonome Systeme 20 Fachbereich Informatik > Künstliche Intelligenz und Maschinelles Lernen Forschungsfelder Forschungsfelder > Information and Intelligence Forschungsfelder > Information and Intelligence > Cognitive Science Zentrale Einrichtungen Zentrale Einrichtungen > hessian.AI - Hessisches Zentrum für Künstliche Intelligenz |
| Hinterlegungsdatum: | 13 Mai 2022 13:20 |
| Letzte Änderung: | 17 Mai 2022 12:29 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
- ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition. (deposited 13 Mai 2022 13:20) [Gegenwärtig angezeigt]
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum