Abstract:
Facial emotion recognition is a crucial task in the realm of computer vision and artificial intelligence, facilitating applications such as human-computer interaction, emotion-aware computing, and sentiment analysis. In this research, we propose a Convolutional Neural Network (CNN) architecture with eight hidden layers for accurate and efficient facial emotion recognition. The CNN model is designed to automatically extract intricate facial features and learn intricate patterns associated with diverse emotional states from input facial images. To train and validate the model, a comprehensive dataset encompassing a wide range of facial expressions representing various emotions is employed. The dataset is carefully preprocessed to ensure homogeneity and minimize any biases that may arise during training. To ensure the CNN's optimal performance and prevent overfitting, we employ various optimization techniques and regularization methods during the training process. Additionally, data augmentation techniques are employed to augment the dataset, enabling the model to generalize effectively to unseen samples. The experimental results demonstrate the efficacy of our CNN architecture, achieving an impressive accuracy of 98.19% on the facial emotion recognition task. The high accuracy attained highlights the robustness of the proposed model and its potential for real-world applications where precise emotion recognition is paramount to enhancing human-machine interactions. Our research contributes to the advancement of facial emotion recognition technology by presenting a deep learning-based approach using a CNN model with eight hidden layers. The achieved accuracy of 98.19% showcases the model's effectiveness, making it a valuable tool for emotion analysis and understanding in various artificial intelligence systems and applications.
