Interpretable Image Emotion Recognition: A Domain Adaptation Approach Using Facial Expressions

• URL: http://arxiv.org/abs/2011.08388v3
• Date: Wed, 28 Aug 2024 22:05:07 GMT
• Title: Interpretable Image Emotion Recognition: A Domain Adaptation Approach Using Facial Expressions
• Authors: Puneet Kumar, Balasubramanian Raman,
• Abstract summary: This paper proposes a feature-based domain adaptation technique for identifying emotions in generic images. It addresses the challenge of the limited availability of pre-trained models and well-annotated datasets for Image Emotion Recognition (IER) The proposed IER system demonstrated emotion classification accuracies of 60.98% for the IAPSa dataset, 58.86% for the ArtPhoto dataset, 69.13% for the FI dataset, and 58.06% for the EMOTIC dataset.
• Score: 11.808447247077902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a feature-based domain adaptation technique for identifying emotions in generic images, encompassing both facial and non-facial objects, as well as non-human components. This approach addresses the challenge of the limited availability of pre-trained models and well-annotated datasets for Image Emotion Recognition (IER). Initially, a deep-learning-based Facial Expression Recognition (FER) system is developed, classifying facial images into discrete emotion classes. Maintaining the same network architecture, this FER system is then adapted to recognize emotions in generic images through the application of discrepancy loss, enabling the model to effectively learn IER features while classifying emotions into categories such as 'happy,' 'sad,' 'hate,' and 'anger.' Additionally, a novel interpretability method, Divide and Conquer based Shap (DnCShap), is introduced to elucidate the visual features most relevant for emotion recognition. The proposed IER system demonstrated emotion classification accuracies of 60.98% for the IAPSa dataset, 58.86% for the ArtPhoto dataset, 69.13% for the FI dataset, and 58.06% for the EMOTIC dataset. The system effectively identifies the important visual features leading to specific emotion classifications and provides detailed embedding plots to explain the predictions, enhancing the understanding and trust in AI-driven emotion recognition systems.

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