Spotting the Details: The Various Facets of Facial Expressions

Grewe, Carl Martin; Roux, G.; Pilz, Sven-Kristofer; Zachow, Stefan

doi:10.1109/fg.2018.00049

Cited by 4 publications

(5 citation statements)

References 30 publications

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“…Such setup could be part of motion capture technologies that translate the movements of the person's face into digitally constructed displays of emotion (Zhang, Snavely, N., Curless, B., & Seitz, 2004). Those have the advantage that certain features can be dealt with in a post-productive manner when building generative and/or morphable face models (e.g., Grewe, Le Roux, Pilz, & Zachow, 2018), thereby providing fine-grained control over the type and dynamics of facial actions that drive response classification. Generative approaches such as the one pursued by Yu, Garrodd, and Schyns (2012) also allow for facial models that are constructed based on ecologically valid facial movements, with the liberty to synthesize arbitrary facial expressions from parameterized movements.…”

Section: Discussionmentioning

confidence: 99%

Human and machine validation of 14 databases of dynamic facial expressions

et al. 2020

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With a shift in interest toward dynamic expressions, numerous corpora of dynamic facial stimuli have been developed over the past two decades. The present research aimed to test existing sets of dynamic facial expressions (published between 2000 and 2015) in a cross-corpus validation effort. For this, 14 dynamic databases were selected that featured facial expressions of the basic six emotions (anger, disgust, fear, happiness, sadness, surprise) in posed or spontaneous form. In Study 1, a subset of stimuli from each database (N = 162) were presented to human observers and machine analysis, yielding considerable variance in emotion recognition performance across the databases. Classification accuracy further varied with perceived intensity and naturalness of the displays, with posed expressions being judged more accurately and as intense, but less natural compared to spontaneous ones. Study 2 aimed for a full validation of the 14 databases by subjecting the entire stimulus set (N = 3812) to machine analysis. A FACS-based Action Unit (AU) analysis revealed that facial AU configurations were more prototypical in posed than spontaneous expressions. The prototypicality of an expression in turn predicted emotion classification accuracy, with higher performance observed for more prototypical facial behavior. Furthermore, technical features of each database (i.e., duration, face box size, head rotation, and motion) had a significant impact on recognition accuracy. Together, the findings suggest that existing databases vary in their ability to signal specific emotions, thereby facing a trade-off between realism and ecological validity on the one end, and expression uniformity and comparability on the other.

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Section: Discussionmentioning

confidence: 99%

Human and machine validation of 14 databases of dynamic facial expressions

et al. 2020

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“…The faces in the database consist of textured surface meshes with a varying amount of vertices, but statistical analysis requires a fixed amount across all scans. A common surface mesh with 1,827 vertices and 1,750 quads is transferred to all scans using the approach proposed by Grewe et al (2018). As shown in Figure 4, we used the face mesh of the MakeHuman 2 project since it is suitable for a broad range of applications, including VR.…”

Section: Data and Preprocessingmentioning

confidence: 99%

“…In contrast to existing statistical face models, we aligned all scans to a cranial coordinate system (CCS). A few anthropometric landmarks were used that remain stable even under large expression deformations, such as a wide opening of the mouth (Grewe et al, 2018). An alignment within a CCS is particularly beneficial to facilitate the attachment of additional facial details like the eyes, teeth, or hair.…”

Section: Data and Preprocessingmentioning

confidence: 99%

Statistical Learning of Facial Expressions Improves Realism of Animated Avatar Faces

Grewe

Liu

Kahl

et al. 2021

Front. Virtual Real.

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A high realism of avatars is beneficial for virtual reality experiences such as avatar-mediated communication and embodiment. Previous work, however, suggested that the usage of realistic virtual faces can lead to unexpected and undesired effects, including phenomena like the uncanny valley. This work investigates the role of photographic and behavioral realism of avatars with animated facial expressions on perceived realism and congruence ratings. More specifically, we examine ratings of photographic and behavioral realism and their mismatch in differently created avatar faces. Furthermore, we utilize these avatars to investigate the effect of behavioral realism on perceived congruence between video-recorded physical person’s expressions and their imitations by the avatar. We compared two types of avatars, both with four identities that were created from the same facial photographs. The first type of avatars contains expressions that were designed by an artistic expert. The second type contains expressions that were statistically learned from a 3D facial expression database. Our results show that the avatars containing learned facial expressions were rated more photographically and behaviorally realistic and possessed a lower mismatch between the two dimensions. They were also perceived as more congruent to the video-recorded physical person’s expressions. We discuss our findings and the potential benefit of avatars with learned facial expressions for experiences in virtual reality and future research on enfacement.

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“…Methods exist which can establish facial landmarks and filter-out pose variations from such scans completely (e.g., Claes et al, 2011;Hammond et al, 2004, White et al 2019. It has also been demonstrated that landmarks can be established accurately in fully automatically fashion (Grewe & Zachow, 2016, Grewe et al, 2018. 3D scanning is, however, still rarely used in psychology and photography remains the preferred data acquisition method for various reasons including affordability and ease-of-use.…”

Section: Challenges In Facial Asymmetry Measurementmentioning

confidence: 99%

“…To prepare the 3D scan data for a statistical shape analysis, dense face matching was performed MEASURING FACIAL ASYMMETRY 9 utilizing a framework developed by Grewe and colleagues (Grewe & Zachow, 2016;Grewe et al, 2018). Shape variation was decomposed into symmetric and asymmetric components using the method described by Klingenberg and colleagues (2002).…”

Section: Construction Of a 3d Morphable Face Modelmentioning

confidence: 99%