FN-DeepCNN: Facial Expression Recognition Using Fine-Tuned Deep Convolutional Neural Network

In human-computer interaction, Facial Emotion Recognition (FER) is essential, particularly in fields like behavioral analysis and psychological therapy. Perceiving emotions accurately from facial expressions can enhance communication and interaction between humans and machines. However, the wide range of human faces and image variations, including different lighting conditions and facial poses, makes it difficult to achieve accurate and robust FER using computer models. In this work, we chose to work exclusively on the FER2013 dataset to address its complications and complexities in terms of feature extraction. To analyze its impact, we employed a Deep Convolutional Neural Network (DCNN) and pre-trained models that have been adjusted specifically for emotion recognition. This work takes into account a pre-processing step that concentrates on image resolution, histogram equalization, and data augmentation. We achieved a higher accuracy of 76% with the Inception model using the FER2013 dataset.