Convolutional Neural Network for Facial Emotion Detection

Facial Emotion Recognition is considered one of the important domains in computer vision, which allows machines to interpret human emotions and, consequently, find applications in adaptive gaming, healthcare, and so forth. In this work, a Convolutional Neural Network (CNN)-based approach to emotion classification using FER-2013 dataset is proposed. This dataset contains 35,887 grayscale images with seven categories of emotions such as angry, disgust, fear, happy, sad, surprise, and also neutral. The architecture integrates convolutional layers, max-pooling, dropout regularization, and fully connected layers for robust feature extraction and classification. Data augmentation techniques in terms of rotation, zooming, and flipping are applied to reduce overfitting and enhance generalization. Training uses the Adam optimizer with a decaying learning rate, in addition to categorical cross-entropy loss, in a total number of 30 epochs. Experiments are performed to get a 54.7% accuracy on the validation set; strong performance is achieved for emotions “happy” and “surprise,” whereas “neutral” and “fear” cannot be well distinguished. The experiment stresses that one ought to balance datasets, exploit multimodal data and advanced model optimization techniques for improved accuracy and making models useful in the real world. Findings set the benchmark for upgrades in FER into better robust and scalable systems to cater to varying demands for applications.