Paper Contents
Abstract
A crucial field of computer vision is face expression recognition, which aims to give machines the ability to decipher and comprehend human emotions from facial expressions. In order to classify different facial expressions, this project focuses on developing an emotion recognition system using machine learning algorithms, specifically Convolutional Neural Networks (CNNs). Through the analysis of facial features taken from photos, the system is intended to identify facial expressions like happiness, sadness, anger, surprise, fear, and neutral. The project makes use of publicly accessible datasets, like the FER-2013 dataset, which includes facial image labels that represent various emotional states. The project makes use of CNNs to train the model and predict emotions in real time on fresh images using Python and well-known libraries like OpenCV and TensorFlow. The field has evolved from using handcrafted features to leveraging deep neural networks for automatic feature extraction (Rathour et al., 2022). Future directions include addressing dataset biases, integrating robust models, and exploring multimodal approaches for more effective recognition (Rathour et al., 2022). As FER continues to advance, it promises to enhance emotionally intelligent systems and improve human-machine interactions across various domains
Copyright
Copyright © 2025 Mandula Bala Narasimha Swamy. This is an open access article distributed under the Creative Commons Attribution License.
