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. (Publisher's Version)
In: Frontiers in Robotics and AI, 8, Frontiers Media S.A., e-ISSN 2296-9144,
DOI: 10.26083/tuprints-00020336,
[Article]

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.

Item Type: Article
Erschienen: 2022
Creators: Rawal, Niyati ; Koert, Dorothea ; Turan, Cigdem ; Kersting, Kristian ; Peters, Jan ; Stock-Homburg, Ruth
Origin: Secondary publication DeepGreen
Status: Publisher's Version
Title: ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition
Language: English
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.

Journal or Publication Title: Frontiers in Robotics and AI
Volume of the journal: 8
Publisher: Frontiers Media S.A.
Collation: 11 Seiten
Uncontrolled Keywords: facial expression generation, humanoid robots, facial expression recognition, neural networks, gradient descent
Divisions: 01 Department of Law and Economics
01 Department of Law and Economics > Betriebswirtschaftliche Fachgebiete
01 Department of Law and Economics > Betriebswirtschaftliche Fachgebiete > Department of Marketing & Human Resource Management
20 Department of Computer Science
20 Department of Computer Science > Intelligent Autonomous Systems
20 Department of Computer Science > Artificial Intelligence and Machine Learning
Forschungsfelder
Forschungsfelder > Information and Intelligence
Forschungsfelder > Information and Intelligence > Cognitive Science
Zentrale Einrichtungen
Zentrale Einrichtungen > hessian.AI - The Hessian Center for Artificial Intelligence
Date Deposited: 13 May 2022 13:20
DOI: 10.26083/tuprints-00020336
URL / URN: https://tuprints.ulb.tu-darmstadt.de/20336
URN: urn:nbn:de:tuda-tuprints-203368
PPN:
Corresponding Links:
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details