Face Recognition for Human-Robot Interaction Applications: A Comparative Study

Author(s):  
Mauricio Correa ◽  
Javier Ruiz-del-Solar ◽  
Fernando Bernuy
Author(s):  
Nikolaos Mavridis ◽  
Michael Petychakis ◽  
Alexandros Tsamakos ◽  
Panos Toulis ◽  
Shervin Emami ◽  
...  

AbstractThe overarching goal of the FaceBots project is to support the achievement of sustainable long-term human-robot relationships through the creation of robots with face recognition and natural language capabilities, which exploit and publish online information, and especially social information available on Facebook, and which achieve two significant novelties. The underlying experimental hypothesis is that such relationships can be significantly enhanced if the human and the robot are gradually creating a pool of episodic memories that they can co-refer to (“shared memories”), and if they are both embedded in a social web of other humans and robots they mutually know (“shared friends”). We present a description of system architecture, as well as important concrete results regarding face recognition and transferability of training, with training and testing sets coming from either one or a combination of two sources: an onboard camera which can provide sequences of images, as well as facebook-derived photos. Furthermore, early interaction-related results are presented, and evaluation methodologies as well as interesting extensions are discussed.


Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 267
Author(s):  
Fernando Alonso Martin ◽  
María Malfaz ◽  
Álvaro Castro-González ◽  
José Carlos Castillo ◽  
Miguel Ángel Salichs

The success of social robotics is directly linked to their ability of interacting with people. Humans possess verbal and non-verbal communication skills, and, therefore, both are essential for social robots to get a natural human–robot interaction. This work focuses on the first of them since the majority of social robots implement an interaction system endowed with verbal capacities. In order to do this implementation, we must equip social robots with an artificial voice system. In robotics, a Text to Speech (TTS) system is the most common speech synthesizer technique. The performance of a speech synthesizer is mainly evaluated by its similarity to the human voice in relation to its intelligibility and expressiveness. In this paper, we present a comparative study of eight off-the-shelf TTS systems used in social robots. In order to carry out the study, 125 participants evaluated the performance of the following TTS systems: Google, Microsoft, Ivona, Loquendo, Espeak, Pico, AT&T, and Nuance. The evaluation was performed after observing videos where a social robot communicates verbally using one TTS system. The participants completed a questionnaire to rate each TTS system in relation to four features: intelligibility, expressiveness, artificiality, and suitability. In this study, four research questions were posed to determine whether it is possible to present a ranking of TTS systems in relation to each evaluated feature, or, on the contrary, there are no significant differences between them. Our study shows that participants found differences between the TTS systems evaluated in terms of intelligibility, expressiveness, and artificiality. The experiments also indicated that there was a relationship between the physical appearance of the robots (embodiment) and the suitability of TTS systems.


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