Comparison Between the Facial Flow Lines of Androids and Humans

Ishihara, Hisashi; Iwanaga, Saneyuki; Asada, Minoru

doi:10.3389/frobt.2021.540193

Cited by 9 publications

(7 citation statements)

References 21 publications

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“…The coder found it difficult to differentiate AUs 6 (cheek raiser) and 7 (lid tightener). This is in line with earlier findings that androids struggled to replicate z-vector movements, including wrinkles and tension, compared with human expressions (Ishihara et al, 2021), owing to the physical constraints of artificial skin materials. The results of our intensity evaluation revealed that some AUs' maximum intensities were not realized.…”

Section: Discussionsupporting

confidence: 92%

An Android for Emotional Interaction: Spatiotemporal Validation of Its Facial Expressions

et al. 2022

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Android robots capable of emotional interactions with humans have considerable potential for application to research. While several studies developed androids that can exhibit human-like emotional facial expressions, few have empirically validated androids’ facial expressions. To investigate this issue, we developed an android head called Nikola based on human psychology and conducted three studies to test the validity of its facial expressions. In Study 1, Nikola produced single facial actions, which were evaluated in accordance with the Facial Action Coding System. The results showed that 17 action units were appropriately produced. In Study 2, Nikola produced the prototypical facial expressions for six basic emotions (anger, disgust, fear, happiness, sadness, and surprise), and naïve participants labeled photographs of the expressions. The recognition accuracy of all emotions was higher than chance level. In Study 3, Nikola produced dynamic facial expressions for six basic emotions at four different speeds, and naïve participants evaluated the naturalness of the speed of each expression. The effect of speed differed across emotions, as in previous studies of human expressions. These data validate the spatial and temporal patterns of Nikola’s emotional facial expressions, and suggest that it may be useful for future psychological studies and real-life applications.

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Section: Discussionsupporting

confidence: 92%

An Android for Emotional Interaction: Spatiotemporal Validation of Its Facial Expressions

et al. 2022

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“…These AUs appear to reflect facial tension rather than facial movement, suggesting that not only the movement vector, which can be detected by the landmark detection algorithm, but also the approach, which can capture undulations in facial movements, will be necessary. In fact, recent studies have suggested that human faces have distinctive undulations when expressing each facial component [ 52 ]. Therefore, further development of RGB analyses or other information to subtly differentiate these AUs is desired.…”

Section: Discussionmentioning

confidence: 99%

Assessing Automated Facial Action Unit Detection Systems for Analyzing Cross-Domain Facial Expression Databases

Namba¹,

Sato²,

Osumi³

et al. 2021

Sensors

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In the field of affective computing, achieving accurate automatic detection of facial movements is an important issue, and great progress has already been made. However, a systematic evaluation of systems that now have access to the dynamic facial database remains an unmet need. This study compared the performance of three systems (FaceReader, OpenFace, AFARtoolbox) that detect each facial movement corresponding to an action unit (AU) derived from the Facial Action Coding System. All machines could detect the presence of AUs from the dynamic facial database at a level above chance. Moreover, OpenFace and AFAR provided higher area under the receiver operating characteristic curve values compared to FaceReader. In addition, several confusion biases of facial components (e.g., AU12 and AU14) were observed to be related to each automated AU detection system and the static mode was superior to dynamic mode for analyzing the posed facial database. These findings demonstrate the features of prediction patterns for each system and provide guidance for research on facial expressions.

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“…Then, we used AdvancedSkeleton to add controllers to these points, in order to enable facial expression simulation in the humanoid head model. The movement displacements of the different parts were determined based on the research conducted by Ishihara et al [ 35 ], who tracked the three-dimensional positions of 125 Japanese male faces. Furthermore, when simulating the six basic facial expressions, the simulation was carried out with the aim of achieving the maximum intensity of each facial expression.…”

Section: Mechanical Design and Facial Expression Realization Of The H...mentioning

confidence: 99%

Facial Expression Realization of Humanoid Robot Head and Strain-Based Anthropomorphic Evaluation of Robot Facial Expressions

Yan,

Song,

Zhou

et al. 2024

Biomimetics

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The facial expressions of humanoid robots play a crucial role in human–computer information interactions. However, there is a lack of quantitative evaluation methods for the anthropomorphism of robot facial expressions. In this study, we designed and manufactured a humanoid robot head that was capable of successfully realizing six basic facial expressions. The driving force behind the mechanism was efficiently transmitted to the silicone skin through a rigid linkage drive and snap button connection, which improves both the driving efficiency and the lifespan of the silicone skin. We used human facial expressions as a basis for simulating and acquiring the movement parameters. Subsequently, we designed a control system for the humanoid robot head in order to achieve these facial expressions. Moreover, we used a flexible vertical graphene sensor to measure strain on both the human face and the silicone skin of the humanoid robot head. We then proposed a method to evaluate the anthropomorphic degree of the robot’s facial expressions by using the difference rate of strain. The feasibility of this method was confirmed through experiments in facial expression recognition. The evaluation results indicated a high degree of anthropomorphism for the six basic facial expressions which were achieved by the humanoid robot head. Moreover, this study also investigates factors affecting the reproduction of expressions. Finally, the impulse was calculated based on the strain curves of the energy consumption of the humanoid robot head to complete different facial expressions. This offers a reference for fellow researchers when designing humanoid robot heads, based on energy consumption ratios. To conclude, this paper offers data references for optimizing the mechanisms and selecting the drive components of the humanoid robot head. This was realized by considering the anthropomorphic degree and energy consumption of each part. Additionally, a new method for evaluating robot facial expressions is proposed.

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Comparison Between the Facial Flow Lines of Androids and Humans

Cited by 9 publications

References 21 publications

An Android for Emotional Interaction: Spatiotemporal Validation of Its Facial Expressions

An Android for Emotional Interaction: Spatiotemporal Validation of Its Facial Expressions

Assessing Automated Facial Action Unit Detection Systems for Analyzing Cross-Domain Facial Expression Databases

Facial Expression Realization of Humanoid Robot Head and Strain-Based Anthropomorphic Evaluation of Robot Facial Expressions

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