Domain Adaptation based Technique for Image Emotion Recognition using Pre-trained Facial Expression Recognition Models

In this paper, a domain adaptation based technique for recognizing the emotions in images containing facial, non-facial, and non-human components has been proposed. We have also proposed a novel technique to explain the proposed system's predictions in terms of Intersection Score. Image emotion recognition is useful for graphics, gaming, animation, entertainment, and cinematography. However, well-labeled large scale datasets and pre-trained models are not available for image emotion recognition. To overcome this challenge, we have proposed a deep learning approach based on an attentional convolutional network that adapts pre-trained facial expression recognition models. It detects the visual features of an image and performs emotion classification based on them. The experiments have been performed on the Flickr image dataset, and the images have been classified in 'angry,' 'happy,' 'sad,' and 'neutral' emotion classes. The proposed system has demonstrated better performance than the benchmark results with an accuracy of 63.87% for image emotion recognition. We have also analyzed the embedding plots for various emotion classes to explain the proposed system's predictions.