A Survey on Infant Emotion Recognition through Video Clips

Vankudre, Gauri; Ghulaxe, Vikram; Dhomane, Ankit; Badlani, Sagar; Rane, Tushar

doi:10.1109/iccike51210.2021.9410786

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…In [31] , a dataset containing infant cries was recorded by the authors, and traditional machine learning algorithms were utilised to detect the cry signal. Also in [36] the authors studied how recognition of infants' emotion is necessary for the overall development, through audio / cry signals. The authors in [37] proposed Automated Facial Action Recognition (AFAR), trained using CNN across infant databases performed better compared to training database specific AU detectors which was trained on adult faces.…”

Section: Related Workmentioning

confidence: 99%

An ensemble multi-stream classifier for infant needs detection

Fahmy¹,

Fahmy²,

García³

et al. 2023

Heliyon

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Section: Related Workmentioning

confidence: 99%

An ensemble multi-stream classifier for infant needs detection

Fahmy¹,

Fahmy²,

García³

et al. 2023

Heliyon

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

Khan,

Xu,

Liu

et al. 2024

Multimedia Systems

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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.

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A Pragmatic Approach for Infant Cry Analysis Using Support Vector Machine and Random Forest Classifiers

Basavaiah,

Anthony

2024

Wireless Pers Commun

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A Survey on Infant Emotion Recognition through Video Clips

Cited by 3 publications

References 24 publications

An ensemble multi-stream classifier for infant needs detection

An ensemble multi-stream classifier for infant needs detection

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

A Pragmatic Approach for Infant Cry Analysis Using Support Vector Machine and Random Forest Classifiers

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