Cross-cultural emotion recognition and evaluation of Radboud faces database with an Indian sample

Abstract

Emotional databases are important tools to study emotion recognition and their effects on various cognitive processes. Since, well-standardized large-scale emotional expression database is not available in India, we evaluated Radboud faces database (RaFD)—a freely available database of emotional facial expressions of adult Caucasian models, for Indian sample. Using the pictures from RaFD, we investigated the similarity and differences in self-reported ratings on emotion recognition accuracy as well as parameters of valence, clarity, genuineness, intensity and arousal of emotional expression, by following the same rating procedure as used for the validation of RaFD. We also systematically evaluated the universality hypothesis of emotion perception by analyzing differences in accuracy and ratings for different emotional parameters across Indian and Dutch participants. As the original Radboud database lacked arousal rating, we added this as a emotional parameter along with all other parameters. The results show that the overall accuracy of emotional expression recognition by Indian participants was high and very similar to the ratings from Dutch participants. However, there were significant cross-cultural differences in classification of emotion categories and their corresponding parameters. Indians rated certain expressions comparatively more genuine, higher in valence, and less intense in comparison to original Radboud ratings. The misclassifications/ confusion for specific emotional categories differed across the two cultures indicating subtle but significant differences between the cultures. In addition to understanding the nature of facial emotion recognition, this study also evaluates and enables the use of RaFD within Indian population.

Recognizing Emotions through Facial Expressions: A Largescale Experimental Study

“…Taking advantage of its characteristics, various studies on emotion recognition have used the RaFD for specific purposes [ 47 , 48 , 49 , 50 , 51 ]. Recently there have been increased efforts to validate this database with different populations, such as Indian participants [ 52 ] and children [ 53 ], both with good results. In the first case, despite the significant cross-cultural differences in the classification of emotions that should be taken in account, the authors found high recognition agreement rates [ 52 ], which were similar to the ones reported in the original study by Langner et al (2010) [ 32 ].…”

“…Recently there have been increased efforts to validate this database with different populations, such as Indian participants [ 52 ] and children [ 53 ], both with good results. In the first case, despite the significant cross-cultural differences in the classification of emotions that should be taken in account, the authors found high recognition agreement rates [ 52 ], which were similar to the ones reported in the original study by Langner et al (2010) [ 32 ]. In the second case, children’s emotion recognition pattern was identical to the adults’ pattern, although children were less able to distinguish between similar emotions [ 54 ].…”

Recognition of emotions through facial expression analysis

“…Ultimately, among the main findings of this systematic review, we identified that culture can modify the facial expressions that a person exhibits when feeling an emotion (Mishra et al, 2018). It highlights the possibility that there may be a modification in a person's facial expressions depending on whether the emotion they are feeling is real or acted out (Tarnowski et al, 2017).…”

“…On the other hand, the conception of a universal emotional language is challenged, (Jack et al, 2012), determine that culture can modify the facial expressions that people exhibit when feeling an emotion, an idea shared by (Mishra et al, 2018), who point out the existence of subtle but significant cross-cultural differences when correctly classifying emotions, following this same line (Barrett et al, 2019), remarking that the way of communicating an emotion varies significantly depending on the culture or situation in which the individual is.…”

The Accuracy Validation of FireFACE Software in Analyzing Facial Expressions of Urban Forest Visitors

“…The top 20 relevant studies with specific source of data (eight from figure, words, or tables) were documented for data extraction. The standard for employment screening was adapted as the mean of 20 studies (Table 1) [14,[28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46]. The specific process of this part of study is shown in Figure 3.…”