The facial expression recognition (FER) task is crucial in the field of computer vision, with applications ranging from human-computer interaction to mental health assessment. Thanks to the application of convolutional neural networks, deep learning has advanced significantly in recent years, making advancement in numerous fields, including FER. These achievements have led to high classification accuracy in emotion recognition. However, understanding how the model operates and makes decisions remains a highly complex process. This project provides an overview of 3 different saliency map generation methods including GradCam, Bubbles and External Perturbation performed on YOLOv8 exploring the field of explainable AI in FER.