Real-Time Facial Expression Recognition for Natural Interaction

Cerezo, Eva; Hupont, Isabelle; Manresa-Yee, Cristina; Varona, Javier; Baldassarri, Sandra; Perales, F.; Serón, Francisco J.

doi:10.1007/978-3-540-72849-8_6

Cited by 18 publications

(10 citation statements)

References 7 publications

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“…The emotional classification system in this ontology database is based on the further improvement of six categories of emotional classification system of Ekman. [14][15][16] Finally, the emotional category "good" was added to the noumenon of vocabulary, and the praise emotion in Chinese was divided into 7 categories (pleasure, good, anger, sadness, fear, disgust, and surprise) and 21 more detailed categories (including praise, boredom, derogation, hatred, etc.). There are seven types of parts of speech in emotional vocabulary ontology: noun, verb, adj, adv, nw, idiom, and prep.…”

Section: Construction Of Emotional Words Dictionarymentioning

confidence: 99%

Emotional characteristics and time series analysis of Internet public opinion participants based on emotional feature words

Jia

Chen

2020

International Journal of Advanced Robotic Systems

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In recent years, with the rapid development and wide application of the Internet, it has become the main place for the generation and dissemination of public opinion. To grasp the information of network public opinion in a timely and comprehensive way can not only effectively prevent sudden network malignant events but also provide a reference for the scientific and democratic decision-making of government departments. Therefore, in view of the practical application needs, this article studies the emotional characteristics and the evolution of public opinion over time based on the emotional feature words of network public opinion participants. Firstly, the positive and negative emotional lexicon of HowNet emotional dictionary is used, and the commonly used emotional lexicon and expression symbols are added to the lexicon. At the same time, the polarity annotation method of Chinese emotional lexicon ontology is used to construct the emotional lexicon of this article. Secondly, considering other emotional polarity characteristics in the dictionary, an emotional tendency analysis model is proposed. In this article, emotional analysis is applied to the evolution analysis of network public opinion, and the change of network public opinion characteristics with time series is obtained. The simulation results show that the emotional dictionary constructed in this article and the proposed model of emotional orientation analysis can effectively analyze the emotional characteristics of network public opinion participants and apply emotional analysis to the evolution analysis of network public opinion, which can get the change of emotional characteristics of public opinion participants with time series.

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Section: Construction Of Emotional Words Dictionarymentioning

confidence: 99%

Emotional characteristics and time series analysis of Internet public opinion participants based on emotional feature words

Jia

Chen

2020

International Journal of Advanced Robotic Systems

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“…Besides recognizing the user's facial expressions with a webcam (Cerezo et al, 2007) new modules have being added to the system for the detection of affective information. In particular, affective cues are extracted from user's typed-in text, and right now a keyboard pulses and mouse clicks analyzer is being developed to detect states of boredom, confusion, frustration and nervousness of the user.…”

Section: Maxine: An Animation Engine For Multimodal Emotional Communimentioning

confidence: 99%

Maxine: Embodied conversational agents for multimodal affective communication

Baldassarri¹,

Cerezo²

2012

Computer Graphics

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“…The images used in Experiment 1, plus the expressions for: gratification, hope, liking, pride, relief, remorse, resentment, and shame were validated using an automatic recognizer developed by Cerezo et al 16 The method studies the variation of a certain number of face parameters (distances and angles between some feature points of the face) with respect to the neutral expression. The characteristic points are based on the points defined on the MPEG-4 standard.…”

Section: Objective Evaluation By Automatic Recognizermentioning

confidence: 99%

Generation and visualization of emotional states in virtual characters

Arellano

Varona²,

Perales³

2008

Computer Animation & Virtual

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This paper presents an affective model that determines the emotional state of a character according to the personality traits and the experienced emotions. We consider an emotional state as the layer between personality and emotion. The proposed affective model offers a mapping between emotions and emotional states. To evidence emotional states of a virtual character, we can attribute them facial expressions based on their associated emotions. Facial expressions for intermediate emotions are generated automatically from expressions for universal emotions. The experiments show coherent emotional states produced by a simulated story. They also present how the corresponding emotions were represented through dynamic and static facial expressions. Finally, the obtained results demonstrate the satisfactory recognition by a group of people unfamiliar with the work described. Copyright

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Real-Time Facial Expression Recognition for Natural Interaction

Cited by 18 publications

References 7 publications

Emotional characteristics and time series analysis of Internet public opinion participants based on emotional feature words

Emotional characteristics and time series analysis of Internet public opinion participants based on emotional feature words

Maxine: Embodied conversational agents for multimodal affective communication

Generation and visualization of emotional states in virtual characters

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