Review on Emotion Recognition Databases

Haamer, Rain Eric; Rusadze, Eka; Lüsi, Iiris; Ahmed, Toufik; SergioEscalera,; Anbarjafari, Gholamreza

doi:10.5772/intechopen.72748

Cited by 42 publications

(23 citation statements)

References 91 publications

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“…However, given the highly contextual nature of facial expression recognition [20], controlled laboratory settings are ideal for identifying AUs that are specific to perceived core affective processes such as positive and negative affect. Further, most valence-detecting CVML models assume a unidimensional valence continuum as opposed to separable continua for positive and negative affect—to our knowledge, there are few opensource datasets used in CVML research that characterize valence as multi-dimensional (see [34]), and very little work has been done with CVML to separate positive and negative affect (cf. [35]).…”

Section: Introductionmentioning

confidence: 99%

Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity

et al. 2019

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Facial expressions are fundamental to interpersonal communication, including social interaction, and allow people of different ages, cultures, and languages to quickly and reliably convey emotional information. Historically, facial expression research has followed from discrete emotion theories, which posit a limited number of distinct affective states that are represented with specific patterns of facial action. Much less work has focused on dimensional features of emotion, particularly positive and negative affect intensity. This is likely, in part, because achieving inter-rater reliability for facial action and affect intensity ratings is painstaking and labor-intensive. We use computer-vision and machine learning (CVML) to identify patterns of facial actions in 4,648 video recordings of 125 human participants, which show strong correspondences to positive and negative affect intensity ratings obtained from highly trained coders. Our results show that CVML can both (1) determine the importance of different facial actions that human coders use to derive positive and negative affective ratings when combined with interpretable machine learning methods, and (2) efficiently automate positive and negative affect intensity coding on large facial expression databases. Further, we show that CVML can be applied to individual human judges to infer which facial actions they use to generate perceptual emotion ratings from facial expressions.

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Section: Introductionmentioning

confidence: 99%

Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity

et al. 2019

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“…SAVEE DATASET Berlin Database was created in 1999 and consists of utterances spoken by various actors. EMODB has different number of spoken utterances for seven emotions [34].Emotions included in the database are anger, boredom, disgust, fear, happiness, indifference, sadness. The dataset contains more than 500 utterances spoken by 51 male and 60 female actors from the age 21 to 35 years.…”

Section: B1 Statistical Data Corpusmentioning

confidence: 99%

Big Data Classification for the Analysis MEL Scale Features Using KNN Parameterization

Osadchyy¹,

Skuratovskii

2020

International Journal of Circuits, Systems and Signal Processing

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The role of human speech is intensified by the emotion it conveys. The parameterization of the vector obtained from the sentence divided into the containing emotional-informational part and the informational part is effectively applied. There are several characteristics and features of speech that differentiate it among utterances, i.e. various prosodic features like pitch, timbre, loudness and vocal tone which categorize speech into several emotions. They were supplemented by us with a new classification feature of speech, which consists in dividing a sentence into an emotionally loaded part of the sentence and a part that carries only informational load. Therefore, the sample speech is changed when it is subjected to various emotional environments. As the identification of the speaker’s emotional states can be done based on the Mel scale, MFCC is one such variant to study the emotional aspects of a speaker’s utterances. In this work, we implement a model to identify several emotional states from MFCC for two datasets, classify emotions for them on the basis of MFCC features and give the comparison of both. Overall, this work implements the classification model based on dataset minimization that is done by taking the mean of features for the improvement of the classification accuracy rate in different machine learning algorithms.

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“…However, to be useful for the majority of applications within the cognitive, decision, and other social and behavioral sciences—where controlled laboratory settings are the norm—it is only necessary that CVML models are optimized for performances within laboratory settings as opposed to in the real-world. Lastly, most valence-detecting CVML models assume a unidimensional valence continuum as opposed to separable continua for positive and negative affect—to our knowledge, there are few opensource datasets used in CVML research that characterize valence as multi-dimensional (see 33), and very little work has been done with CVML to separate positive and negative affect (cf. 34).…”

Section: Using Computer-vision and Machine Learning To Automate Faciamentioning

confidence: 99%

Using Computer-vision and Machine Learning to Automate Facial Coding of Positive and Negative Affect Intensity

Haines

Southward

Cheavens

et al. 2018

Preprint

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Facial expressions are fundamental to interpersonal communication, including social interaction, and allow people of different ages, cultures, and languages to quickly and reliably convey emotional information. Historically, facial expression research has followed from discrete emotion theories, which posit a limited number of distinct affective states that are represented with specific patterns of facial action. Much less work has focused on dimensional features of emotion, particularly positive and negative affect intensity. This is likely, in part, because achieving inter-rater reliability for facial action and affect intensity ratings is painstaking and labor-intensive. We use computer-vision and machine learning (CVML) to identify patterns of facial actions in 4,648 video recordings of 125 human participants, which show strong correspondences to positive and negative affect intensity ratings obtained from highly trained coders. Our results show that CVML can both (1) determine the importance of different facial actions that human coders use to derive positive and negative affective ratings, and (2) efficiently automate positive and negative affect intensity coding on large facial expression databases. Further, we show that CVML can be applied to individual human judges to infer which facial actions they use to generate perceptual emotion ratings from facial expressions.

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Review on Emotion Recognition Databases

Cited by 42 publications

References 91 publications

Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity

Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity

Big Data Classification for the Analysis MEL Scale Features Using KNN Parameterization

Using Computer-vision and Machine Learning to Automate Facial Coding of Positive and Negative Affect Intensity

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