Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

Yalçin, Nursel; Alisawi, Muthana

doi:10.1016/j.heliyon.2024.e38913

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e38913

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Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

Nursel Yalçin,

Muthana Alisawi

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2024

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PortraitEmotion3D: A Novel Dataset and 3D Emotion Estimation Method for Artistic Portraiture Analysis

Liu,

Agaian,

Grigoryan

2024

Applied Sciences

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Facial Expression Recognition (FER) has been widely explored in realistic settings; however, its application to artistic portraiture presents unique challenges due to the stylistic interpretations of artists and the complex interplay of emotions conveyed by both the artist and the subject. This study addresses these challenges through three key contributions. First, we introduce the PortraitEmotion3D (PE3D) dataset, designed explicitly for FER tasks in artistic portraits. This dataset provides a robust foundation for advancing emotion recognition in visual art. Second, we propose an innovative 3D emotion estimation method that leverages three-dimensional labeling to capture the nuanced emotional spectrum depicted in artistic works. This approach surpasses traditional two-dimensional methods by enabling a more comprehensive understanding of the subtle and layered emotions often in artistic representations. Third, we enhance the feature learning phase by integrating a self-attention module, significantly improving facial feature representation and emotion recognition accuracy in artistic portraits. This advancement addresses this domain’s stylistic variations and complexity, setting a new benchmark for FER in artistic works. Evaluation of the PE3D dataset demonstrates our method’s high accuracy and robustness compared to existing state-of-the-art FER techniques. The integration of our module yields an average accuracy improvement of over 1% in recent FER systems. Additionally, combining our method with ESR-9 achieves a comparable accuracy of 88.3% on the FER+ dataset, demonstrating its generalizability to other FER benchmarks. This research deepens our understanding of emotional expression in art and facilitates potential applications in diverse fields, including human–computer interaction, security, healthcare diagnostics, and the entertainment industry.

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PortraitEmotion3D: A Novel Dataset and 3D Emotion Estimation Method for Artistic Portraiture Analysis

Liu,

Agaian,

Grigoryan

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

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Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

Cited by 1 publication

References 30 publications

PortraitEmotion3D: A Novel Dataset and 3D Emotion Estimation Method for Artistic Portraiture Analysis

PortraitEmotion3D: A Novel Dataset and 3D Emotion Estimation Method for Artistic Portraiture Analysis

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