This paper presents an anthropomorphic robotic bear for the exploration of human-robot interaction including verbal and non-verbal communications. This robot is implemented with a hybrid face composed of a mechanical faceplate with 10 DOFs and an LCD-display-equipped mouth. The facial emotions of the bear are designed based on the description of the Facial Action Coding System as well as some animal-like gestures described by Darwin. The mouth movements are realized by synthesizing emotions with speech. User acceptance investigations have been conducted to evaluate the likability of these facial behaviors exhibited by the eBear. Multiple Kernel Learning is proposed to fuse different features for recognizing user's facial expressions. Our experimental results show that the developed Bear-Like robot can perceive basic facial expressions and provide emotive conveyance towards human beings.
Although much progress has been made to design aesthetic animal-like faces of these articulated robots, it is usually difficult to design the mechanic structures with multiple DOFs and jointly control them when displaying several complex facial behaviors such as mouth movement. In this case, the robot BERT2" @cite_1 was designed with a hybrid face including a plastic faceplate and an LCD display, where eye brows, eyes and mouth movement were displayed via graphics animation. The recognizability of BERT2's facial expressions were evaluated and verified through human-robot interaction experiments. Compared to BERT2, our eBear has 10 DOFs on the mechanical faceplate including ears, and it is able to blend the expressive movement with speech during mouth animation, which incorporates the benefits of both mechanical and graphical facial elements.
@cite_1
2014225072 2165942021 219197677 2062024333
The BERT2 social robot, a platform for the exploration of human-robot interaction, is currently being built at the Bristol Robotics Laboratory. This paper describes work on the robot's face, a hybrid face composed of a plastic faceplate and an LCD display, and our implementation of facial expressions on this versatile platform. We report the implementation of two representations of affect space, each of which map the space of potential emotions to specific facial feature parameters and the results of a series of human-robot interaction experiments to characterize the recognizability of the robot's archetypal facial expressions. The tested subjects' recognition of the implemented facial expressions for happy, surprised, and sad was robust (with nearly 100 recognition). Subjects, however, tended to confuse the expressions for disgusted and afraid with other expressions, with correct recognition rates of 21.1 and 52.6 respectively. Future work involves the addition of more realistic eye movements for stronger recognition of certain responses. These results demonstrate that a hybrid face with affect space facial expression implementations can provide emotive conveyance readily recognized by human beings. In this paper, we propose a complete pipeline of efficient and low-cost techniques to construct a realistic 3D text-driven emotive audio-visual avatar from a single 2D frontal-view face image of any person on the fly. This real-time conversion is achieved through three steps. First, a personalized 3D face model is built based on the 2D face image using a fully automatic 3D face shape and texture reconstruction framework. Second, using standard MPEG-4 FAPs (Facial Animation Parameters), the face model is animated by the Viseme and expression channels and is complemented by the visual prosody channel that controls head, eye and eyelid movements. Finally, the facial animation is combined and synchronized with the emotive synthetic speech generated by incorporating an emotion transformer into a Festival-MBROLA text to neutral speech synthesizer. This paper describes our initial work in developing a real-time audio-visual Chinese speech synthesizer with a 3D expressive avatar. The avatar model is parameterized according to the MPEG-4 facial animation standard [1]. This standard offers a compact set of facial animation parameters (FAPs) and feature points (FPs) to enable realization of 20 Chinese visemes and 7 facial expressions (i.e. 27 target facial configurations). The Xface [2] open source toolkit enables us to define the influence zone for each FP and the deformation function that relates them. Hence we can easily animate a large number of coordinates in the 3D model by specifying values for a small set of FAPs and their FPs. FAP values for 27 target facial configurations were estimated from available corpora. We extended the dominance blending approach to effect animations for coarticulated visemes superposed with expression changes. We selected six sentiment-carrying text messages and synthesized expressive visual speech (for all expressions, in randomized order) with neutral audio speech. A perceptual experiment involving 11 subjects shows that they can identify the facial expression that matches the text message’s sentiment 85 of the time. Summary In highly social species such as humans, faces have evolved to convey rich information for social interaction, including expressions of emotions and pain [1–3]. Two motor pathways control facial movement [4–7]: a subcortical extrapyramidal motor system drives spontaneous facial expressions of felt emotions, and a cortical pyramidal motor system controls voluntary facial expressions. The pyramidal system enables humans to simulate facial expressions of emotions not actually experienced. Their simulation is so successful that they can deceive most observers [8–11]. However, machine vision may be able to distinguish deceptive facial signals from genuine facial signals by identifying the subtle differences between pyramidally and extrapyramidally driven movements. Here, we show that human observers could not discriminate real expressions of pain from faked expressions of pain better than chance, and after training human observers, we improved accuracy to a modest 55 . However, a computer vision system that automatically measures facial movements and performs pattern recognition on those movements attained 85 accuracy. The machine system's superiority is attributable to its ability to differentiate the dynamics of genuine expressions from faked expressions. Thus, by revealing the dynamics of facial action through machine vision systems, our approach has the potential to elucidate behavioral fingerprints of neural control systems involved in emotional signaling.