An Assessment of In-the-Wild Datasets for Multimodal Emotion Recognition

Aguilera, Ana; Mellado, Diego; Rojas, Felipe

doi:10.3390/s23115184

Cited by 5 publications

(2 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another study, Li et al, explored the use of multimodal approaches for FER by integrating both facial and physiological signals [50]. Their work demonstrated that combining facial features extracted from deep convolutional neural networks with physiological signals, such as heart rate and electrodermal activity, resulted in enhanced emotion recognition accuracy.…”

Section: An Analysis Of Prior Research In the Relevant Fieldmentioning

confidence: 99%

See 1 more Smart Citation

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

Anbananthan Pillai Munanday,

Norazlianie Sazali,

Arjun Asogan

et al. 2023

ARASET

View full text Add to dashboard Cite

The primary objective of this study is to develop a real-time system that can predict the emotional states of an individual who commonly undergoes various experiences. The primary methodology suggested in this research for detecting facial expressions involves the integration of transfer learning techniques that incorporate convolutional neural networks (CNNs), along with a parameterization approach that minimizes the number of parameters. The FER-2013, JAFFE, and CK+ datasets were jointly used to train the CNN architecture for real-time detection, which broadened the range of emotional expressions that may be recognized. The proposed model has the capability to identify various emotions, including but not limited to happiness, fear, surprise, anger, contempt, sadness, and neutrality. Several methods were employed to assess the efficacy of the model's performance in this study. The experimental results indicate that the proposed approach surpasses previous studies in terms of both speed and accuracy.

show abstract

Section: An Analysis Of Prior Research In the Relevant Fieldmentioning

confidence: 99%

“…Aguilera et al, [50] achieved an accuracy of 63.85% on the Emotion Recognition in the Wild dataset using a transfer learning approach with a pre-trained deep neural network. This was higher than the accuracy achieved by several other approaches.…”

Section: Performance Evaluation Of Transfer Learning For Facial Expre...mentioning

confidence: 99%

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

Anbananthan Pillai Munanday,

Norazlianie Sazali,

Arjun Asogan

et al. 2023

ARASET

View full text Add to dashboard Cite

show abstract

Exploring contactless techniques in multimodal emotion recognition: insights into diverse applications, challenges, solutions, and prospects

Khan,

Xu,

Liu

et al. 2024

Multimedia Systems

View full text Add to dashboard Cite

In recent years, emotion recognition has received significant attention, presenting a plethora of opportunities for application in diverse fields such as human–computer interaction, psychology, and neuroscience, to name a few. Although unimodal emotion recognition methods offer certain benefits, they have limited ability to encompass the full spectrum of human emotional expression. In contrast, Multimodal Emotion Recognition (MER) delivers a more holistic and detailed insight into an individual's emotional state. However, existing multimodal data collection approaches utilizing contact-based devices hinder the effective deployment of this technology. We address this issue by examining the potential of contactless data collection techniques for MER. In our tertiary review study, we highlight the unaddressed gaps in the existing body of literature on MER. Through our rigorous analysis of MER studies, we identify the modalities, specific cues, open datasets with contactless cues, and unique modality combinations. This further leads us to the formulation of a comparative schema for mapping the MER requirements of a given scenario to a specific modality combination. Subsequently, we discuss the implementation of Contactless Multimodal Emotion Recognition (CMER) systems in diverse use cases with the help of the comparative schema which serves as an evaluation blueprint. Furthermore, this paper also explores ethical and privacy considerations concerning the employment of contactless MER and proposes the key principles for addressing ethical and privacy concerns. The paper further investigates the current challenges and future prospects in the field, offering recommendations for future research and development in CMER. Our study serves as a resource for researchers and practitioners in the field of emotion recognition, as well as those intrigued by the broader outcomes of this rapidly progressing technology.

show abstract

A Systematic Review on Multimodal Emotion Recognition: Building Blocks, Current State, Applications, and Challenges

Kalateh,

Estrada-Jimenez,

Nikghadam-Hojjati

et al. 2024

IEEE Access

View full text Add to dashboard Cite

An Assessment of In-the-Wild Datasets for Multimodal Emotion Recognition

Cited by 5 publications

References 84 publications

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

Exploring contactless techniques in multimodal emotion recognition: insights into diverse applications, challenges, solutions, and prospects

A Systematic Review on Multimodal Emotion Recognition: Building Blocks, Current State, Applications, and Challenges

Contact Info

Product

Resources

About