  A domain adaptation technique has been proposed in this paper to identify the
emotions in generic images containing facial & non-facial objects and non-human
components. It addresses the challenge of the insufficient availability of
pre-trained models and well-annotated datasets for image emotion recognition
(IER). It starts with proposing a facial emotion recognition (FER) system and
then moves on to adapting it for image emotion recognition. First, a
deep-learning-based FER system has been proposed that classifies a given facial
image into discrete emotion classes. Further, an image recognition system has
been proposed that adapts the proposed FER system to recognize the emotions
portrayed by images using domain adaptation. It classifies the generic images
into 'happy,' 'sad,' 'hate,' and 'anger' classes. A novel interpretability
approach, Divide and Conquer based Shap (DnCShap), has also been proposed to
interpret the highly relevant visual features for emotion recognition. The
proposed system's architecture has been decided through ablation studies, and
the experiments are conducted on four FER and four IER datasets. The proposed
IER system has shown an emotion classification accuracy of 59.61% for the IAPSa
dataset, 57.83% for the ArtPhoto dataset, 67.93% for the FI dataset, and 55.13%
for the EMOTIC dataset. The important visual features leading to a particular
emotion class have been identified, and the embedding plots for various emotion
classes have been analyzed to explain the proposed system's predictions.
