Tsinghua facial expression database – A database of facial expressions in Chinese young and older women and men: Development and validation

Yang, Tao; Yang, Zeyun; Xu, Guangzheng; Gao, Duoling; Zhang, Ziheng; Wang, Hui; Liu, Shiyu; Han, Linfeng; Zhu, Zhi‐Xin; Tian, Yang; Huang, Yuqi; Zhao, Lei; Zhong, Kui; Shi, Bolin; Li, Juan; Fu, Shimin; Liang, Peipeng; Banissy, Michael J.; Sun, Pei

doi:10.1371/journal.pone.0231304

Cited by 61 publications

(40 citation statements)

References 46 publications

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“…Previous studies have demonstrated that facial recognition is sensitive to differences in the race (Ekman and Friesen, 1971;Ekman et al, 1987;Ng and Lindsay, 1994); therefore, to exclude cross-race influences on facial expression recognition, facial emotional expression images were selected from the Tsinghua facial expression database, which is a database of facial expressions posed by young and older Chinese women and men (Yang et al, 2020). In this study, we selected happy, neutral, and sad expressions with correct categorization rates of 97.77, 84.97, and 76.41%, respectively (Yang et al, 2020). Since the algorithm (Kim et al, 2019) used for real-to-cartoon face conversion in this study was established using female images, all the facial expression images selected from the database were images of young female actors (20 in total).…”

Section: Stimulimentioning

confidence: 99%

The Influence of Key Facial Features on Recognition of Emotion in Cartoon Faces

et al. 2021

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Cartoon faces are widely used in social media, animation production, and social robots because of their attractive ability to convey different emotional information. Despite their popular applications, the mechanisms of recognizing emotional expressions in cartoon faces are still unclear. Therefore, three experiments were conducted in this study to systematically explore a recognition process for emotional cartoon expressions (happy, sad, and neutral) and to examine the influence of key facial features (mouth, eyes, and eyebrows) on emotion recognition. Across the experiments, three presentation conditions were employed: (1) a full face; (2) individual feature only (with two other features concealed); and (3) one feature concealed with two other features presented. The cartoon face images used in this study were converted from a set of real faces acted by Chinese posers, and the observers were Chinese. The results show that happy cartoon expressions were recognized more accurately than neutral and sad expressions, which was consistent with the happiness recognition advantage revealed in real face studies. Compared with real facial expressions, sad cartoon expressions were perceived as sadder, and happy cartoon expressions were perceived as less happy, regardless of whether full-face or single facial features were viewed. For cartoon faces, the mouth was demonstrated to be a feature that is sufficient and necessary for the recognition of happiness, and the eyebrows were sufficient and necessary for the recognition of sadness. This study helps to clarify the perception mechanism underlying emotion recognition in cartoon faces and sheds some light on directions for future research on intelligent human-computer interactions.

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Section: Stimulimentioning

confidence: 99%

The Influence of Key Facial Features on Recognition of Emotion in Cartoon Faces

et al. 2021

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“…All stimuli can be seen on the OSF ( https://osf.io/xsfg8/ ). Original images were drawn from Tsinghua facial expression database ( Yang et al, 2020 ) with permission.…”

Section: Introductionmentioning

confidence: 99%

Effects of wearing a transparent face mask on perception of facial expressions

et al. 2022

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Wearing face masks in public has become the norm in many countries post-2020. Although mask-wearing is effective in controlling infection, it has the negative side effect of occluding the mask wearer’s facial expressions. The purpose of this study was to investigate the effects of wearing transparent masks on the perception of facial expressions. Participants were required to categorize the perceived facial emotion of female (Experiment 1) and male (Experiment 2) faces with different facial expressions and to rate the perceived emotion intensity of the faces. Based on the group, the participants were assigned to, the faces were presented with a surgical mask, a transparent mask, or without a mask. The results showed that wearing a surgical mask impaired the performance of reading facial expressions, both with respect to recognition and perceived intensity of facial emotions. Specifically, the impairments were robustly observed in fear and happy faces for emotion recognition, and in happy faces for perceived intensity of emotion in Experiments 1 and 2. However, the impairments were moderated by wearing a transparent mask instead of a surgical mask. During the coronavirus disease 2019 (COVID-19) pandemic, the transparent mask can be used in a range of situations where face-to-face communication is important.

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“…Similar criteria were used by other dataset creators (e.g. Yang et al, 2020;Ebner, Riediger, & Lindenberger, 2010). At the second phase of ratings, the additional dimensions of attractiveness, valence, and genuineness were added to the survey, which was then taken by 11 raters who each rated all 248 remaining images.…”

Section: Database Validationmentioning

confidence: 99%

“…While there is an agreement that facial movements are informative for inferring emotions, the existence of a precise mapping between configurations of facial movements and emotion categories generalizable to all cultures, remains strongly debated (Nelson & Russel, 2013;Duran & Fernández-Dols, 2018;Barret et al, 2019). Most of the existing emotional face stimulus sets are developed in Western societies and a few countries in East Asia (The EU-emotion stimulus set (O'Reilly et al 2016), Tsinghua facial expression database (Yang et al, 2020),…”

Section: Introductionmentioning

confidence: 99%

Iranian emotional face database: Acquisition and validation of a stimulus set of basic facial expressions

2022

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Facial expressions play an essential role in social interactions. Databases of face images have furnished theories of emotion perception, as well as having applications in other disciplines such as facial recognition technology. However, the faces of many ethnicities remain largely underrepresented in the existing face databases, which can impact the generalizability of the theories and technologies developed based on them. Here, we present the first survey-validated database of Iranian faces. It consists of 248 images from 40 Iranian individuals portraying six emotional expressions --anger, sadness, fear, disgust, happiness, and surpriseas well as the neutral state. The photos were taken in a studio setting, following the common scenarios of emotion induction, and controlling for conditions of lighting, camera set up, and model's head posture. An evaluation survey confirmed a high agreement between the models' intended expressions and the raters' perception of them. The database is freely available online for academic research purposes.

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Tsinghua facial expression database – A database of facial expressions in Chinese young and older women and men: Development and validation

Cited by 61 publications

References 46 publications

The Influence of Key Facial Features on Recognition of Emotion in Cartoon Faces

The Influence of Key Facial Features on Recognition of Emotion in Cartoon Faces

Effects of wearing a transparent face mask on perception of facial expressions

Iranian emotional face database: Acquisition and validation of a stimulus set of basic facial expressions

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