TU Darmstadt / ULB / TUbiblio

ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition

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, 8
doi: 10.3389/frobt.2021.730317
Artikel, Bibliographie

Dies ist die neueste Version dieses Eintrags.

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: Bibliographie
Titel: ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition
Sprache: Englisch
Publikationsjahr: 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: 10.3389/frobt.2021.730317
Zugehörige Links:
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
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: 02 Aug 2024 12:41
Letzte Änderung: 02 Aug 2024 12:41
PPN:
Export:
Suche nach Titel in: TUfind oder in Google

Verfügbare Versionen dieses Eintrags

Frage zum Eintrag Frage zum Eintrag

Optionen (nur für Redakteure)
Redaktionelle Details anzeigen Redaktionelle Details anzeigen