Emotion Recognition for Affect Aware Video Games

Szwoch, Mariusz; Szwoch, Wioleta

doi:10.1007/978-3-319-10662-5_28

Cited by 28 publications

(16 citation statements)

References 13 publications

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“…Emotions that players would like to feel most often during the game are joy (4,55) and surprise (3,59). Eventually, seldom would they like to feel fear (1,55).…”

Section: B Results and Proposalsmentioning

confidence: 99%

“…Secondly, and more important, a player's affect state can change even radically from session to session making it almost impossible to predict the current user's emotions at the development stage. That is why the real-time recognition of player's affect may become important for video games industry in the nearest future [3]. To know what we should focus on while recognizing emotions, the model of game player should be created.…”

Section: Introductionmentioning

confidence: 99%

“…[1], [2]. Affective computing studies how to automatically recognize, interpret, and process human emotions via analyzing available sensory data [3]. The term 'affect' is used in psychology to refer to feelings or emotions.…”

Section: Introductionmentioning

confidence: 99%

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Model of emotions for game players

Szwoch

2015

2015 8th International Conference on Human System Interaction (HSI)

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Affect-aware video games can respond to a game player's emotions. Such games seem to be more attractive for users. Therefore for that kind of games it is necessary to create a model of the player's emotions to know to which emotions the application should react. The paper describes different models of emotions. The questionnaire and experiment for video game players is presented. Some results of the tests are shown. Then the model of game player that allows to describe his or her emotions is proposed. 1

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“…Emotions that players would like to feel most often during the game are joy (4,55) and surprise (3,59). Eventually, seldom would they like to feel fear (1,55).…”

Section: B Results and Proposalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model of emotions for game players

Szwoch

2015

2015 8th International Conference on Human System Interaction (HSI)

Self Cite

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“…Naturally, effective and efficient utilization of the affective context requires affect-aware games to be able to recognize and track players' emotions in real-time [2]. Over the past decade, in-game emotion recognition has successfully been realized in non-commercial, academic projects through the recognition of physiological features [3] and facial expressions [1].…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition through Intentional Context

Ihasz

Kovacs

Kryssanov

2019

IJAE

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Recently there is a growing need for affect-awareness in computer games. In-game emotion recognition, however, requires applying costly feature extraction methods and/or labor-demanding annotation of large datasets. To make emotion recognition cost-efficient, this study proposes (A) a social relations-directing, "emotion-sensitive" dialogue act model consisting of social acts, and (B) an approach for emotion recognition, utilizing the association strength ratio of emotion types and the proposed acts. In the study, five Japanese in-game dialogues were tagged with labels of emotion types and of social acts. Emotion type-social act co-occurrence was analyzed, and the corresponding association strength ratios were computed. In a validation experiment, the proposed approach was tested, enhancing a baseline emotion classifier's recognition accuracy by 8%.

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“…Describing emotions with discrete categories such as happiness or anger is useful in human-human and human-computer interactions. For example, an affect-aware game that can measure if the player is happy with the difficulty of the game can make the game more enjoyable [1,2]. An interactive system that can communicate emotions can play a key role in teaching social interactions to children with autism spectrum disorders [3].…”

Section: Introductionmentioning

confidence: 99%

Predicting Categorical Emotions by Jointly Learning Primary and Secondary Emotions through Multitask Learning

Lotfian¹,

Busso²

2018

Interspeech 2018

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Detection of human emotions is an essential part of affect-aware human-computer interaction (HCI). In daily conversations, the preferred way of describing affects is by using categorical emotion labels (e.g., sad, anger, surprise). In categorical emotion classification, multiple descriptors (with different degrees of relevance) can be assigned to a sample. Perceptual evaluations have relied on primary and secondary emotions to capture the ambiguous nature of spontaneous recordings. Primary emotion is the most relevant category felt by the evaluator. Secondary emotions capture other emotional cues also conveyed in the stimulus. In most cases, the labels collected from the secondary emotions are discarded, since assigning a single class label to a sample is preferred from an application perspective. In this work, we take advantage of both types of annotations to improve the performance of emotion classification. We collect the labels from all the annotations available for a sample and generate primary and secondary emotion labels. A classifier is then trained using multitask learning with both primary and secondary emotions. We experimentally show that considering secondary emotion labels during the learning process leads to relative improvements of 7.9% in F1-score for an 8-class emotion classification task.

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Emotion Recognition for Affect Aware Video Games

Cited by 28 publications

References 13 publications

Model of emotions for game players

Model of emotions for game players

Emotion Recognition through Intentional Context

Predicting Categorical Emotions by Jointly Learning Primary and Secondary Emotions through Multitask Learning

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