Physiological-Based Affect Event Detector for Entertainment Video Applications

Fleureau, Julien; Guillotel, Philippe; Huynh-Thu, Quan

doi:10.1109/t-affc.2012.2

Cited by 64 publications

(34 citation statements)

References 16 publications

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“…Indeed, the related works are dissimilar in the modality to recognize the affective states that can be natural or induced. Thus, emotion can be evoked by watching affective movies [20], video clips [21], since playing a video game [22], driving a car or listening to music [23] [24]. Moreover, the emotion can be defined into different models: the first is Eckman's model that is based on universal emotional expressions to present out six discrete basic emotions: Happiness, Sadness, Surprise, Fear, Anger and Disgust [25].…”

Section: Introductionmentioning

confidence: 99%

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Wiem¹,

Lachiri²

2017

ijacsa

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Abstract-Emotion recognition from physiological signals attracted the attention of researchers from different disciplines, such as affective computing, cognitive science and psychology. This paper aims to classify emotional statements using peripheral physiological signals based on arousal-valence evaluation. These signals are the Electrocardiogram, Respiration Volume, Skin Temperature and Galvanic Skin Response. We explored the signals collected in the MAHNOB-HCI multimodal tagging database. We defined the emotion into three different ways: two and three classes using 1-9 discrete self-rating scales and another model using 9 emotional keywords to establish the three defined areas in arousal-valence dimensions. To perform the accuracies, we began by removing the artefacts and noise from the signals, and then we extracted 169 features. We finished by classifying the emotional states using the support vector machine. The obtained results showed that the electrocardiogram and respiration volume were the most relevant signals for human's feeling recognition task. Moreover, the obtained accuracies were promising comparing to recent related works for each of the three establishments of emotion modeling.

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Section: Introductionmentioning

confidence: 99%

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Wiem¹,

Lachiri²

2017

ijacsa

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“…Experimental results on a dataset of 64 scenes from eight movies watched by eight participants demonstrated that in addition to video features, subjects' physiological responses (i.e., GSR, EMG, blood pressure, respiration, and ST) could provide affective ranking to video scenes. Fleureau et al [97] proposed a two-stage affect detector for video viewing and entertainment applications. They first recognized affective events in the videos, and then use Gaussian processes to classify the video segments as positive or negative using GSR, heart rate, and electromyogram.…”

Section: Implicit Video Affective Content Analysis Using Physiologicamentioning

confidence: 99%

“…In addition, they used different methods for emotion dimension prediction, such as linear relevance vector machine [91], and emotion category classification, such as decision tree [13], Gaussian process classifiers [97] and SVM [96], [106] etc.…”

Section: Implicit Video Affective Content Analysis Using Physiologicamentioning

confidence: 99%

Video Affective Content Analysis: A Survey of State-of-the-Art Methods

Wang

2015

IEEE Trans. Affective Comput.

163

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Video affective content analysis has been an active research area in recent decades, since emotion is an important component in the classification and retrieval of videos. Video affective content analysis can be divided into two approaches: direct and implicit. Direct approaches infer the affective content of videos directly from related audiovisual features. Implicit approaches, on the other hand, detect affective content from videos based on an automatic analysis of a user's spontaneous response while consuming the videos. This paper first proposes a general framework for video affective content analysis, which includes video content, emotional descriptors, and users' spontaneous nonverbal responses, as well as the relationships between the three. Then, we survey current research in both direct and implicit video affective content analysis, with a focus on direct video affective content analysis. Lastly, we identify several challenges in this field and put forward recommendations for future research.Index Terms-Video affective content analysis, emotion recognition, and content-based video retrieval.

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“…Chêne et al [27] used physiological linkage between differ- Petridis and Pantic proposed a method for tagging videos for the level of hilarity by analyzing user's laughter [5]. Different types of laughter can be an indicator of the level of hilarity of multimedia content.…”

Section: State Of the Artmentioning

confidence: 99%

Human-centered implicit tagging: Overview and perspectives

Soleymani

Pantić

2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Abstract-Tags are an effective form of metadata which help users to locate and browse multimedia content of interest. Tags can be generated by users (user-generated explicit tags), automatically from the content (content-based tags), or assigned automatically based on non-verbal behavioral reactions of users to multimedia content (implicit human-centered tags). This paper discusses the definition and applications of implicit humancentered tagging. Implicit tagging is an effortless process by which content is tagged based on users' spontaneous reactions. It is a novel but growing research topic which is attracting more attention with the growing availability of built-in sensors. This paper discusses the state of the art in this novel field of research and provides an overview of publicly available relevant databases and annotation tools. We finally discuss in detail challenges and opportunities in the field.

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Physiological-Based Affect Event Detector for Entertainment Video Applications

Cited by 64 publications

References 16 publications

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Video Affective Content Analysis: A Survey of State-of-the-Art Methods

Human-centered implicit tagging: Overview and perspectives

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