Consistent but modest

D’Mello, Sidney K.; Kory, Jacqueline

doi:10.1145/2388676.2388686

Cited by 65 publications

(16 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Audiovisual emotion databases play a key role in emotion recognition for model training and evaluation. Numerous achievements have been reported on the collection of the emotion databases [12, 24, 25]. For instance, the Association for the Advancement of Affective Computing (AAAC), formerly the HUMAINE Association, provided several multimodal, speech, and facial expression databases for association members.…”

Section: Audiovisual Emotion Databasesmentioning

confidence: 99%

“…Table 1 summarizes the databases and some of the noteworthy data resources for audiovisual emotion recognition task. This table describes the following information about each database: Name of database, Language of recordings, Affective state elicitation method (posed (acted), induced or spontaneous emotional expression) [12, 25–27], Number of subjects, Number of available data samples, Affective state description (category, dimension or event) [12, 25, 28], Availability, Publication year, and Challenges used or references reported. …”

Section: Audiovisual Emotion Databasesmentioning

confidence: 99%

“…Affective state elicitation method (posed (acted), induced or spontaneous emotional expression) [12, 25–27],…”

Section: Audiovisual Emotion Databasesmentioning

confidence: 99%

“…The affective state elicitation methods for the collection of the audiovisual databases reported in the literature can be classified into three major categories: (a) posed (acted), (b) induced (via clips), and (c) spontaneous (occurring during an interaction) [12, 25–27]. With regard to the posed databases [29, 47], every actor was asked to correctly express each emotion.…”

Section: Audiovisual Emotion Databasesmentioning

confidence: 99%

See 3 more Smart Citations

Survey on audiovisual emotion recognition: databases, features, and data fusion strategies

Lin

Wei

2014

SIP

142

View full text Add to dashboard Cite

Emotion recognition is the ability to identify what people would think someone is feeling from moment to moment and understand the connection between his/her feelings and expressions. In today's world, human-computer interaction (HCI) interface undoubtedly plays an important role in our daily life. Toward harmonious HCI interface, automated analysis and recognition of human emotion has attracted increasing attention from the researchers in multidisciplinary research fields. In this paper, a survey on the theoretical and practical work offering new and broad views of the latest research in emotion recognition from bimodal information including facial and vocal expressions is provided. First, the currently available audiovisual emotion databases are described. Facial and vocal features and audiovisual bimodal data fusion methods for emotion recognition are then surveyed and discussed. Specifically, this survey also covers the recent emotion challenges in several conferences. Conclusions outline and address some of the existing emotion recognition issues.

show abstract

Section: Audiovisual Emotion Databasesmentioning

confidence: 99%

Section: Audiovisual Emotion Databasesmentioning

confidence: 99%

“…Affective state elicitation method (posed (acted), induced or spontaneous emotional expression) [12, 25–27],…”

Section: Audiovisual Emotion Databasesmentioning

confidence: 99%

Section: Audiovisual Emotion Databasesmentioning

confidence: 99%

See 2 more Smart Citations

Survey on audiovisual emotion recognition: databases, features, and data fusion strategies

Lin

Wei

2014

SIP

142

View full text Add to dashboard Cite

show abstract

“…Multimodal data fusion strategies established in recent years attracted the attention of the researchers in intelligent computing technology. Existing fusion techniques of audiovisual emotion data can be categorized into feature‐level fusion, decision‐level fusion, model‐level fusion and hybrid approaches . In feature‐level fusion, significant features from face and audio were concatenated to construct a joint feature vector before classification.…”

Section: Related Workmentioning

confidence: 99%

Bimodal recognition of affective states with the features inspired from human visual and auditory perception system

Arivazhagan¹,

Russel²

2019

Int J Imaging Syst Tech

View full text Add to dashboard Cite

In this article, attention‐based mechanism with the enhancement on biologically inspired network for emotion recognition is proposed. Existing bio‐inspired models use multiscale and multiorientation architecture to gain discriminative power and to extract meticulous visual features. Prevailing HMAX model represents S2 layers by randomly selected prototype patches from training samples that increase the computational complexity and degrade the discerning ability. As eyes and mouth regions are the most powerful and reliable cues in determining facial emotions, they serve as the prototype patches for S2 layer in HMAX model. Audio code 4 book is constructed from mel‐frequency cepstral coefficients, temporal and spectral features processed by principal component analysis. Audio and video data features are fused to train support vector machine classifier. The attained results on eNTERFACE, surrey audio‐visual expressed emotion and acted facial expressions in the wild database datasets ascertain the efficiency of the proposed architecture for emotion recognition.

show abstract

Multimodal emotion recognition: A comprehensive review, trends, and challenges

Ramaswamy,

Palaniswamy

2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Automatic emotion recognition is a burgeoning field of research and has its roots in psychology and cognitive science. This article comprehensively reviews multimodal emotion recognition, covering various aspects such as emotion theories, discrete and dimensional models, emotional response systems, datasets, and current trends. This article reviewed 179 multimodal emotion recognition literature papers from 2017 to 2023 to reflect on the current trends in multimodal affective computing. This article covers various modalities used in emotion recognition based on the emotional response system under four categories: subjective experience comprising text and self‐report; peripheral physiology comprising electrodermal, cardiovascular, facial muscle, and respiration activity; central physiology comprising EEG, neuroimaging, and EOG; behavior comprising facial, vocal, whole‐body behavior, and observer ratings. This review summarizes the measures and behavior of each modality under various emotional states. This article provides an extensive list of multimodal datasets and their unique characteristics. The recent advances in multimodal emotion recognition are grouped based on the research focus areas such as emotion elicitation strategy, data collection and handling, the impact of culture and modality on multimodal emotion recognition systems, feature extraction, feature selection, alignment of signals across the modalities, and fusion strategies. The recent multimodal fusion strategies are detailed in this article, as extracting shared representations of different modalities, removing redundant features from different modalities, and learning critical features from each modality are crucial for multimodal emotion recognition. This article summarizes the strengths and weaknesses of multimodal emotion recognition based on the review outcome, along with challenges and future work in multimodal emotion recognition. This article aims to serve as a lucid introduction, covering all aspects of multimodal emotion recognition for novices.This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Technologies > Cognitive Computing Technologies > Artificial Intelligence

show abstract

Consistent but modest

Cited by 65 publications

References 35 publications

Survey on audiovisual emotion recognition: databases, features, and data fusion strategies

Survey on audiovisual emotion recognition: databases, features, and data fusion strategies

Bimodal recognition of affective states with the features inspired from human visual and auditory perception system

Multimodal emotion recognition: A comprehensive review, trends, and challenges

Contact Info

Product

Resources

About