Emotion Recognition Using Focal Network and Resnet

Image emotion recognition involves finding the emotions from visual data, usually done through convolutional neural networks (CNN) or deep neural networks (DNN). The existing methodologies are often high complex or time complex and it is not giving the good considerable results and computationally expensive due to high number of parameters. In this paper we try to accomplish a better output by using focal network which stood among other transformers architecture due to the unique property of the dynamic attention Recent studies have shown the efficacy of transformer architectures in various deep learning tasks and challenged the convolution neural networks on various tasks. but, It has a tradeoff between reduced parameter complexity and the need for extensive training data. Drawing inspiration from recent publications, the study adopts a focal network for training, demonstrating its effectiveness in enhancing the model's attention and optimizing emotional feature extraction. The proposed methodology aims to do the process of image emotion recognition by using the advantages of transformers while mitigating the computational problem associated with the deep neural networks. The incorporation of a focal network addresses attention-related challenges, and it contributes to the development of more efficient emotion recognition systems. This research offers insights into the evolving landscape of image emotion recognition methodologies and underscores the potential of transformer architectures in advancing this field.