ARCHIVES

: This research explores emotion recognition using Convolutional Neural Networks (CNNs) with a focus on analyzing the FER2013 dataset. A detailed examination of the model’s architecture, training process, and performance evaluation is presented. The study aims to contribute to the field of emotion recognition and provide insights into the effectiveness of CNNs in this domain. This research delves into the realm of emotion recognition, employing Convolutional Neural Networks (CNNs) with a primary focus on analyzing the FER2013 dataset. Our study introduces a novel CNN architecture designed for accurate classification of seven distinct emotions: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. The research embarks on an in-depth exploration of the model’s intricacies, training methodologies, and performance evaluation metrics.