Facial Emotion Recognition in-the-Wild Using Deep Neural Networks: A Comprehensive Review

Boughanem, Hadjer; Ghazouani, Haythem; Barhoumi, Walid

doi:10.1007/s42979-023-02423-7

SN COMPUT. SCI.

2023

DOI: 10.1007/s42979-023-02423-7

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Facial Emotion Recognition in-the-Wild Using Deep Neural Networks: A Comprehensive Review

Hadjer Boughanem,

Haythem Ghazouani,

Walid Barhoumi

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2024

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Cited by 3 publications

References 79 publications

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A multi-task meta-learner-based ensemble for robust facial expression recognition in-the-wild

Khelifa,

Ghazouani,

Barhoumi

2024

Evol. Intel.

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A multi-task meta-learner-based ensemble for robust facial expression recognition in-the-wild

Khelifa,

Ghazouani,

Barhoumi

2024

Evol. Intel.

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A Facial Morphology-Guided Feature Selection Method For Spontaneous Expression Recognition

Sidhom,

Ghazouani,

Barhoumi

et al. 2024

Procedia Computer Science

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Real-Time Analysis of Facial Expressions for Mood Estimation

Filippini,

Varona,

Manresa-Yee

2024

Applied Sciences

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This paper proposes a model-based method for real-time automatic mood estimation in video sequences. The approach is customized by learning the person’s specific facial parameters, which are transformed into facial Action Units (AUs). A model mapping for mood representation is used to describe moods in terms of the PAD space: Pleasure, Arousal, and Dominance. From the intersection of these dimensions, eight octants represent fundamental mood categories. In the experimental evaluation, a stimulus video randomly selected from a set prepared to elicit different moods was played to participants, while the participant’s facial expressions were recorded. From the experiment, Dominance is the dimension least impacted by facial expression, and this dimension could be eliminated from mood categorization. Then, four categories corresponding to the quadrants of the Pleasure–Arousal (PA) plane, “Exalted”, “Calm”, “Anxious” and “Bored”, were defined, with two more categories for the “Positive” and “Negative” signs of the Pleasure (P) dimension. Results showed a 73% of coincidence in the PA categorization and a 94% in the P dimension, demonstrating that facial expressions can be used to estimate moods, within these defined categories, and provide cues for assessing users’ subjective states in real-world applications.

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Facial Emotion Recognition in-the-Wild Using Deep Neural Networks: A Comprehensive Review

Cited by 3 publications

References 79 publications

A multi-task meta-learner-based ensemble for robust facial expression recognition in-the-wild

A multi-task meta-learner-based ensemble for robust facial expression recognition in-the-wild

A Facial Morphology-Guided Feature Selection Method For Spontaneous Expression Recognition

Real-Time Analysis of Facial Expressions for Mood Estimation

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