Abstract

For many years, scientists have studied the way people express their emotions through body language and facial expressions. However, it is extremely difficult to accurately interpret the emotions of a person from just a single image. Interpreting facial emotions in photographs is a complex task. It is challenging to accurately detect facial emotions with the help of neural networks when the face is occluded with fragmentary blocks. With the advent of technology, emotion detection has become more accurate and reliable. It is now possible to use facial expression recognition in images to detect emotions such as happiness, sadness, anger, fear, surprise, and more. This research discusses the effectiveness of using neural networks to identify facial emotions in photographs with occlusions present. The datasets like Fer2013 dataset, CREMA-D and RAVDESS were used to train the model and the datasets were altered by implanting occlusions randomly in the images. The altered datasets were also used to evaluate the model. The challenges and opportunities that arise when neural networks are used in this context are explored. Additionally, insight is also provided into the best approach to accomplish the task.

Keywords: Neural Networks, Deep Learning; Occlusions; Emotion Interpretation; Human-Computer Interaction