Benchmarking commercial emotion detection systems using realistic distortions of facial image datasets

Yang, Kangning; Wang, Chaofan; Sarsenbayeva, Zhanna; Tag, Benjamin; Dingler, Tilman; Wadley, Greg; Gonçalves, Jorge

doi:10.1007/s00371-020-01881-x

Cited by 42 publications

(34 citation statements)

References 83 publications

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“…Participants reported feeling relieved and less stressed after playing video games. This finding indicates that playing video games could be used as means of digital emotion regulation, as one of the significant features of digital emotion regulation tools is their all-time accessibility and availability on a personal device, e.g., smartphone [43,47,48]. This accessibility and the availability of different sensing modalities to detect emotions, such as frontfacing cameras [36], potentially enables smartphones to proactively provide emotion regulation tools, similar to cognition-aware recommender systems [8].…”

Section: Discussionmentioning

confidence: 98%

Using Video Games to Regulate Emotions

Sarsenbayeva

Tag

Yan

et al. 2020

32nd Australian Conference on Human-Computer Interaction

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In this paper we investigate the practice of using video games for digital emotion regulation. In a two-week diary study we collected participants' records on their emotional states and video games experience. Our findings show that people use video games to respond to their reality, to socialise and communicate with their friends. We also show that video games lead to intentional and unintentional emotional outcomes depending on different aspects of the game. Our work provides insights on using video games for digital emotion regulation and directions for future research.

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Section: Discussionmentioning

confidence: 98%

Using Video Games to Regulate Emotions

Sarsenbayeva

Tag

Yan

et al. 2020

32nd Australian Conference on Human-Computer Interaction

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“…Validation was determined by the 'matching accuracy' variable, which is the matching percentage of the number of photos that were correctly recognized by the instrument for the predicted emotional expressions of prototypical faces for each of the 20 images [31][32][33]. Therefore, matching accuracy can be regarded as the percentage for correct matching.…”

Section: Validation Of Matching Accuracymentioning

confidence: 99%

“…The 20 most relevant studies with specific sources of data (either from figure, words, or tables) were documented for data extraction. The criteria when screening for usage was adapted as the mean of 20 studies (Table 1) [16,[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48]. The specific process is shown in Fig 2 . Only expressions that passed the criteria were used for further assessment.…”

Section: Validation Of Matching Accuracymentioning

confidence: 99%

Facial expressions of Asian people exposed to constructed urban forests: Accuracy validation and variation assessment

et al. 2021

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An outcome of building sustainable urban forests is that people’s well-being is improved when they are exposed to trees. Facial expressions directly represents one’s inner emotions, and can be used to assess real-time perception. The emergence and change in the facial expressions of forest visitors are an implicit process. As such, the reserved character of Asians requires an instrument rating to accurately recognize expressions. In this study, a dataset was established with 2,886 randomly photographed faces from visitors at a constructed urban forest park and at a promenade during summertime in Shenyang City, Northeast China. Six experts were invited to choose 160 photos in total with 20 images representing one of eight typical expressions: angry, contempt, disgusted, happy, neutral, sad, scared, and surprised. The FireFACE ver. 3.0 software was used to test hit-ratio validation as an accuracy measurement (ac.) to match machine-recognized photos with those identified by experts. According to the Kruskal-Wallis test on the difference from averaged scores in 20 recently published papers, contempt (ac. = 0.40%, P = 0.0038) and scared (ac. = 25.23%, P = 0.0018) expressions do not pass the validation test. Both happy and sad expression scores were higher in forests than in promenades, but there were no difference in net positive response (happy minus sad) between locations. Men had a higher happy score but lower disgusted score in forests than in promenades. Men also had a higher angry score in forests. We conclude that FireFACE can be used for analyzing facial expressions in Asian people within urban forests. Women are encouraged to visit urban forests rather than promenades to elicit more positive emotions.

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“…The validation was made with the detection of the variable of 'matching accuracy', which is the matching percentage of the number of photos that were correctly recognized by instrument for the most prototypical expression for each of the 20 images with the predicted emotional expressions [28][29][30]. Therefore, the validation of matching accuracy can be regarded as the percent value for correct matching.…”

Section: Validation Of Matching Accuracymentioning

confidence: 99%

“…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.…”

Section: Validation Of Matching Accuracymentioning

confidence: 99%

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

Wei¹,

Ping²

2020

Preprint

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The construction of sustainable urban forests follows the principle that well-being in people is promoted when exposed to tree population. Facial expression is the direct representation of inner emotion that can be used to assess real-time perception in urban forests. The emergence and change of facial expressions for forest visitors are in an implicit process. The reserved character of oriental races strengthens the requirement for the accuracy to recognize expressions through instrument rating. In this study, a dataset was established with 2,886 randomly photographed faces from visitors in a constructed urban forest park and a promenade at summertime in Shenyang City at Northeast China. Six experts were invited to choose 160 photos in total with 20 images representing one of eight typical expressions as angry, contempt, disgusted, happy, neutral, sad, scared, and surprised emotions. The FireFACE ver. 3.0 software was used to test hit-ratio validation as the accuracy (ac.) to match machine-recognized photos with those identified by experts. According to Kruskal-Wallis test on the difference from averaged scores in 20 recently published papers, contempt (ac.=0.40%, P=0.0038) and scared (ac.=25.23%, P=0.0018) expressions cannot pass the validation test. Therefore, the FireFACE can be used as an instrument to analyze facial expression from oriental people in urban forests but contempt and scared expressions cannot be identified.

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Benchmarking commercial emotion detection systems using realistic distortions of facial image datasets

Cited by 42 publications

References 83 publications

Using Video Games to Regulate Emotions

Using Video Games to Regulate Emotions

Facial expressions of Asian people exposed to constructed urban forests: Accuracy validation and variation assessment

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

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