Real-Time Facial Expression Recognition Using a Fuzzy Emotion Model

Esau, Natascha; Wetzel, Evgenija; Kleinjohann, Lisa; Kleinjohann, Bernd

doi:10.1109/fuzzy.2007.4295451

Cited by 57 publications

(37 citation statements)

References 16 publications

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“…A fuzzy classifier trained on a set of first order sentiments is then able to refine its output by interpreting it in terms of second order sentiments [14]. The same approach is adopted with a view to reproduce facial expression of sentiment blends and complex sentiments [15].…”

Section: B Blending Componentsmentioning

confidence: 99%

Expressions of graduality for sentiments analysis — A survey

Dzogang

Lesot

Rifqi

et al. 2010

International Conference on Fuzzy Systems

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Given the very ambiguous and imprecise nature of sentiments and of their expressions, this survey focuses on approaches making use of components of graduality in the task of automatic sentiments analysis. To that aim, we review methods taking account of intrinsic psychological models components of graduality as well as extrinsic components issued from computational intelligence approaches. In particular, beyond psychological models of sentiments that define affective states as multidimensional vectors in affective continuous spaces, we identify three components of graduality, namely composition or blending, intensity and inheritance. In our discussion, we review how fuzzy set theory as well as other gradual structures based on a vectorial representation are employed to describe affective states as complex or imprecise entities. Finally, we focus on verbal expressions of sentiments and more specifically, we discuss the use of components of graduality in order to deal with sentiments complex and subtle expressions issued from the expressive power of natural languages.

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Section: B Blending Componentsmentioning

confidence: 99%

Expressions of graduality for sentiments analysis — A survey

Dzogang

Lesot

Rifqi

et al. 2010

International Conference on Fuzzy Systems

View full text Add to dashboard Cite

show abstract

“…The problem of the computational cost can be resolved by introducing a more concise 2D facial model. 7,8 In the 2D facial model, for example, the positional relations between salient points on some facial parts (e.g., end points of eyes and eyebrows) are used as facial features. These facial features are fully con-cise to make the computational cost reasonable.…”

Section: Related Workmentioning

confidence: 99%

Recognition and Intensity Estimation of Facial Expression Using Ensemble Classifiers

Nomiya¹,

Sakaue²,

Hochin³

2016

IJNDC

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Facial expression recognition (FER) has been widely studied since it can be used for various applications. However, most of FER techniques focus on discriminating typical facial expressions such as six basic facial expressions. Spontaneous facial expressions are not limited to such typical ones because the intensity of a facial expression varies depending on the intensity of an emotion. In order to utilize FER for real-world applications, therefore, it is necessary to discriminate slight difference of facial expressions. In this paper, we propose an effective FER method to recognize spontaneous facial expressions using ensemble learning which combines a number of naive Bayes classifiers. In addition, a method to estimate the intensity of facial expression is also proposed by using the classification results of the classifiers. The effectiveness of these methods are evaluated through an FER experiment and an experiment to estimate the intensity of facial expressions using a data set including spontaneous facial expressions.

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“…An intelligent system for facial emotion recognition was developed by many researchers using Fuzzy Logic [11], Neural Network [12,13] and Neurofuzzy Logic [14]. Support vector machine (SVM), which were introduced from statistical learning theory by Vapnik (1995), have received considerable attention & have been extensively used in features classification.…”

Section: Facial Emotion Recognitionmentioning

confidence: 99%

Biologically Inspired Computing System for Facial Emotion Detection: A Design Approach

Navghare¹,

Giripunje²,

Bajaj³

2012

IJCA

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The bio-inspired computation algorithms are the excellent tools for global optimization. These algorithms are very easy to understand and simple to implement. These algorithms especially dominate the classical optimization methods. Particle Swarm Optimization (PSO) is a global optimization algorithm that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. This paper presents facial emotion recognition system using PSO for different video clips with different cameras, at different distances and light intensity changes. The analysis is done for recognizing "Happy" emotion from other emotions. The comparison of facial emotion recognition system using other techniques and PSO is done. It is found that PSO has better results over other techniques in terms of accuracy and time required.

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Real-Time Facial Expression Recognition Using a Fuzzy Emotion Model

Cited by 57 publications

References 16 publications

Expressions of graduality for sentiments analysis — A survey

Expressions of graduality for sentiments analysis — A survey

Recognition and Intensity Estimation of Facial Expression Using Ensemble Classifiers

Biologically Inspired Computing System for Facial Emotion Detection: A Design Approach

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Real-Time Facial Expression Recognition Using a Fuzzy Emotion Model

Cited by 57 publications

References 16 publications

Expressions of graduality for sentiments analysis &#x2014; A survey

Expressions of graduality for sentiments analysis &#x2014; A survey

Recognition and Intensity Estimation of Facial Expression Using Ensemble Classifiers

Biologically Inspired Computing System for Facial Emotion Detection: A Design Approach

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Product

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Expressions of graduality for sentiments analysis — A survey

Expressions of graduality for sentiments analysis — A survey