论文标题
可解释的图像情感识别：使用面部表情的域适应方法
Interpretable Image Emotion Recognition: A Domain Adaptation Approach Using Facial Expressions
论文作者
论文摘要
本文提出了一种基于特征的域适应技术，用于识别通用图像中的情绪，包括面部和非种族对象以及非人类组件。这种方法解决了预先训练模型的有限可用性和图像情感识别（IER）的良好数据集的挑战。最初，开发了基于深度学习的面部表达识别（FER）系统，将面部图像分类为离散的情感类别。然后，通过应用差异丢失，可以通过保持相同的网络体系结构，以适应通用图像中的情绪，从而使模型能够有效地学习IER特征，同时将情绪分类为“幸福，'sad'''sale'''''''''''''''''''''''''''和'愤怒。此外，还引入了一种新颖的可解释性方法，划分和征服基于征服的外形（DNCHAP），以阐明与情感识别最相关的视觉特征。提出的IER系统显示IAPSA数据集的情绪分类精度为61.86％，ARTPHOTO数据集的情感精度为62.47，FI数据集的情绪分类精度为70.78％，情绪数据集为59.72％。该系统有效地标识了导致特定情感分类的重要视觉特征，还提供了详细的嵌入图，以解释预测，从而增强了对AI驱动的情感识别系统的理解和信任。
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 61.86% for the IAPSa dataset, 62.47 for the ArtPhoto dataset, 70.78% for the FI dataset, and 59.72% for the EMOTIC dataset. The system effectively identifies the important visual features that lead to specific emotion classifications and also provides detailed embedding plots explaining the predictions, enhancing the understanding and trust in AI-driven emotion recognition systems.
