Extending the MaqFACS to measure facial movement in Japanese macaques (Macaca fuscata) reveals a wide repertoire potential

Correia-Caeiro, Catia; Holmes, Kathryn V.; Miyabe‐Nishiwaki, Takako

doi:10.1371/journal.pone.0245117

Cited by 19 publications

(20 citation statements)

References 88 publications

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“…We tested inter-observer reliability between three FACS coders (CCC: certified in HumanFACS [ 3 ] and in all the AnimalFACS developed to date [ 8 , 9 , 11 – 16 ]; DAW: certified in MaqFACS [ 9 ] and ChimpFACS [ 8 ]; and AA: certified in MaqFACS [ 9 ]) by coding 24 short clips (not used to describe the AUs). Inter-observer reliability was used to: (1) confirm all coders could reliably identify AUs included on the CalliFACS manual, and (2) to refine the descriptions of AUs through discussion when agreement between coders on a particular AU was low.…”

Section: Methodsmentioning

confidence: 99%

“…Following the same approach as the human system, FACS has been modified for use with several other primate species: chimpanzees (ChimpFACS [ 8 ]), rhesus [ 9 ], Barbary [ 10 ] and Japanese macaques [ 11 ] (MaqFACS), hylobatids (GibbonFACS [ 12 ]), and orangutans (OrangFACS [ 13 ]), and three domesticated species: dogs (DogFACS [ 14 ]), horses (EquiFACS [ 15 ]), and cats (CatFACS [ 16 ]). The adaptation of FACS for other species is based on the examination of anatomical homologies (e.g.…”

Section: Introductionmentioning

confidence: 99%

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CalliFACS: The common marmoset Facial Action Coding System

et al. 2022

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Facial expressions are subtle cues, central for communication and conveying emotions in mammals. Traditionally, facial expressions have been classified as a whole (e.g. happy, angry, bared-teeth), due to automatic face processing in the human brain, i.e., humans categorise emotions globally, but are not aware of subtle or isolated cues such as an eyebrow raise. Moreover, the same facial configuration (e.g. lip corners pulled backwards exposing teeth) can convey widely different information depending on the species (e.g. humans: happiness; chimpanzees: fear). The Facial Action Coding System (FACS) is considered the gold standard for investigating human facial behaviour and avoids subjective interpretations of meaning by objectively measuring independent movements linked to facial muscles, called Action Units (AUs). Following a similar methodology, we developed the CalliFACS for the common marmoset. First, we determined the facial muscular plan of the common marmoset by examining dissections from the literature. Second, we recorded common marmosets in a variety of contexts (e.g. grooming, feeding, play, human interaction, veterinary procedures), and selected clips from online databases (e.g. YouTube) to identify their facial movements. Individual facial movements were classified according to appearance changes produced by the corresponding underlying musculature. A diverse repertoire of 33 facial movements was identified in the common marmoset (15 Action Units, 15 Action Descriptors and 3 Ear Action Descriptors). Although we observed a reduced range of facial movement when compared to the HumanFACS, the common marmoset’s range of facial movements was larger than predicted according to their socio-ecology and facial morphology, which indicates their importance for social interactions. CalliFACS is a scientific tool to measure facial movements, and thus, allows us to better understand the common marmoset’s expressions and communication. As common marmosets have become increasingly popular laboratory animal models, from neuroscience to cognition, CalliFACS can be used as an important tool to evaluate their welfare, particularly in captivity.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CalliFACS: The common marmoset Facial Action Coding System

et al. 2022

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“…The systems require training and can be time consuming, however, and reliability is not always easy to achieve (Molina et al, 2019). FACS also might not capture the specific details that scientists might be interested in, and for most understudied primate species, there is still no FACS developed (although attempts to integrate FACS systems developed for different, closely related species have been successful, Correia-Caeiro, Holmes, & Miyabe-Nishiwaki, 2021b;Julle-Danière et al, 2015). It can be difficult to use the data resulting from FACS and create meaningful units for analysis, although there have been some good efforts to create indices of expressivity and diversity from FACS data (Scheider et al, 2014)…”

Section: Methodological Reasonsmentioning

confidence: 99%

The face is central to primate multicomponent signals

et al. 2022

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A wealth of experimental and observational evidence suggests that faces have become increasingly important in the communication system of primates over evolutionary time and that both the static and moveable aspects of faces convey considerable information. Therefore, whenever there is a visual component to any multicomponent signal the face is potentially relevant. However, the role of the face is not always considered in primate multicomponent communication research. We review the literature and make a case for greater focus on the face going forward. We propose that the face can be overlooked for two main reasons: first, due to methodological difficulty. Examination of multicomponent signals in primates is difficult, so scientists tend to examine a limited number of signals in combination. Detailed examination of the subtle and dynamic components of facial signals is particularly hard to achieve in studies of primates. Second, due to a common assumption that the face contains “emotional” content. A priori categorisation of facial behavior as “emotional” ignores the potentially communicative and predictive information present in the face that might contribute to signals. In short, we argue that the face is central to multicomponent signals (and also many multimodal signals) and suggest future directions for investigating this phenomenon.

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“…FACS has been adapted to several animal species, initially for primates (chimpanzee [36], rhesus macaque [37]; orangutan [38], barbary macaque [37], wild crested macaque [39], Japanese macaque [40], gibbon [41]) and the domestic species such as dogs [42], cats [43] and horses [44]. The development of these modified FACS systems was informed by extensive anatomical work, either through dissection [44,45] and/or intramuscular stimulation of facial muscles in living individuals [34,46].…”

Section: Background and Aimmentioning

confidence: 99%

Towards Machine Recognition of Facial Expressions of Pain in Horses

Andersen

Broomé

Rashid

et al. 2021

Animals

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Automated recognition of human facial expressions of pain and emotions is to a certain degree a solved problem, using approaches based on computer vision and machine learning. However, the application of such methods to horses has proven difficult. Major barriers are the lack of sufficiently large, annotated databases for horses and difficulties in obtaining correct classifications of pain because horses are non-verbal. This review describes our work to overcome these barriers, using two different approaches. One involves the use of a manual, but relatively objective, classification system for facial activity (Facial Action Coding System), where data are analyzed for pain expressions after coding using machine learning principles. We have devised tools that can aid manual labeling by identifying the faces and facial keypoints of horses. This approach provides promising results in the automated recognition of facial action units from images. The second approach, recurrent neural network end-to-end learning, requires less extraction of features and representations from the video but instead depends on large volumes of video data with ground truth. Our preliminary results suggest clearly that dynamics are important for pain recognition and show that combinations of recurrent neural networks can classify experimental pain in a small number of horses better than human raters.

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Extending the MaqFACS to measure facial movement in Japanese macaques (Macaca fuscata) reveals a wide repertoire potential

Cited by 19 publications

References 88 publications

CalliFACS: The common marmoset Facial Action Coding System

CalliFACS: The common marmoset Facial Action Coding System

The face is central to primate multicomponent signals

Towards Machine Recognition of Facial Expressions of Pain in Horses

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