What's in a face: Automatic facial coding of untraines study participants compared to standardized inventories

Automatic facial coding (AFC) is a novel research tool to automatically analyze emotional facial expressions. AFC can classify emotional expressions with high accuracy in standardized picture inventories of intensively posed and prototypical expressions. However, classification of facial expressions of untrained study participants is more error prone. This discrepancy requires a direct comparison between these two sources of facial expressions. To this end, 70 untrained participants were asked to express joy, anger, surprise, sadness, disgust, and fear in a typical laboratory setting. Recorded videos were scored with a well-established AFC software (FaceReader, Noldus Information Technology). These were compared with AFC measures of standardized pictures from 70 trained actors (i.e., standardized inventories). We report the probability estimates of specific emotion categories and, in addition, Action Unit (AU) profiles for each emotion. Based on this, we used a novel machine learning approach to determine the relevant AUs for each emotion, separately for both datasets. First, misclassification was more frequent for some emotions of untrained participants. Second, AU intensities were generally lower in pictures of untrained participants compared to standardized pictures for all emotions. Third, although profiles of relevant AU overlapped substantially across the two data sets, there were also substantial differences in their AU profiles. This research provides evidence that the application of AFC is not limited to standardized facial expression inventories but can also be used to code facial expressions of untrained participants in a typical laboratory setting

Com o diabo no corpo: os terríveis papagaios do Brasil colônia

Desde a Antiguidade, papagaios, periquitos e afins (Psittacidae) fascinaram os europeus por seu vivo colorido e uma notável capacidade de interação com seres humanos. A descoberta do Novo Mundo nada faria além de acrescentar novos elementos ao tráfico de animais exóticos há muito estabelecido pelos europeus com a África e o Oriente. Sem possuir grandes mamíferos, a América tropical participaria desse comércio com o que tinha de mais atrativo, essencialmente felinos, primatas e aves - em particular os papagaios, os quais eram embarcados em bom número. Contudo, a julgar pelos documentos do Brasil colônia, esses voláteis podiam inspirar muito pouca simpatia, pois nenhum outro animal - exceto as formigas - foi tantas vezes mencionado como praga para a agricultura. Além disso, alguns psitácidas mostravam-se tão loquazes que inspiravam a séria desconfiança de serem animais demoníacos ou possessos, pois só três classes de entidades - anjos, homens e demônios - possuíam o dom da palavra. Nos dias de hoje, vários representantes dos Psittacidae ainda constituem uma ameaça para a agricultura, enquanto os indivíduos muito faladores continuam despertando a suspeita de estarem possuídos pelo demônio. Transcendendo a mera curiosidade, essa crença exemplifica o quão intrincadas podem ser as relações do homem com o chamado “mundo natural”, revelando um universo mais amplo e multifacetado do que se poderia supor a princípio. Nesse sentido, a existência de aves capazes de falar torna essa relação ainda mais complexa e evidencia que as dificuldades de estabelecer o limite entre o animal e o humano se estendem além dos primatas e envolvem as mais inusitadas espécies zoológicas.Since ancient times, parrots and their allies (Psittacidae) have fascinated Europeans by their striking colors and notable ability to interact with human beings. The discovery of the New World added new species to the international exotic animal trade, which for many centuries had brought beasts to Europe from Africa and the Orient. Lacking large mammals, tropical America participated in this trade with its most appealing species, essentially felines, primates and birds - especially parrots - which were shipped in large numbers. It should be noted, however, that at times these birds were not well liked. In fact, according to documents from colonial Brazil, only the ants rank higher than parrots as the animals most often mentioned as agricultural pests. On the other hand, some of these birds were so chatty that people suspected them to be demonic or possessed animals, since only three classes of beings - angels, men and demons - have the ability to speak. Nowadays, several Psittacidae still constitute a threat to agriculture, and the suspicion that extremely talkative birds were demon possessed has also survived. More than a joke or a mere curiosity, this belief exemplifies how intricate man’s relationships with the “natural world” may be. In this sense, the existence of birds that are able to speak adds a further twist to these relationships, demonstrating that the problem of establishing a boundary between the animal and the human does not only involve primates, but also includes some unusual zoological species

Automatic facial expression recognition in standardized and non-standardized emotional expressions

Emotional facial expressions can inform researchers about an individual's emotional state. Recent technological advances open up new avenues to automatic Facial Expression Recognition (FER). Based on machine learning, such technology can tremendously increase the amount of processed data. FER is now easily accessible and has been validated for the classification of standardized prototypical facial expressions. However, applicability to more naturalistic facial expressions still remains uncertain. Hence, we test and compare performance of three different FER systems (Azure Face API, Microsoft; Face++, Megvii Technology; FaceReader, Noldus Information Technology) with human emotion recognition (A) for standardized posed facial expressions (from prototypical inventories) and (B) for non-standardized acted facial expressions (extracted from emotional movie scenes). For the standardized images, all three systems classify basic emotions accurately (FaceReader is most accurate) and they are mostly on par with human raters. For the non-standardized stimuli, performance drops remarkably for all three systems, but Azure still performs similarly to humans. In addition, all systems and humans alike tend to misclassify some of the non-standardized emotional facial expressions as neutral. In sum, emotion recognition by automated facial expression recognition can be an attractive alternative to human emotion recognition for standardized and non-standardized emotional facial expressions. However, we also found limitations in accuracy for specific facial expressions; clearly there is need for thorough empirical evaluation to guide future developments in computer vision of emotional facial expressions.publishe