Emotion Detection Using Conditional Generative Adversarial Networks (cGAN): A Deep Learning Approach

--Emotion recognition is a key task in affective computing with applications in healthcare, human-computer interaction, and surveillance systems. This study proposes a Conditional Generative Adversarial Network (cGAN)-based approach to generate synthetic emotion-specific facial images to augment training data and mitigate class imbalance. The generator learns to synthesize grayscale 64 64 facial images conditioned on emotion labels, while the discriminator distinguishes between real and generated images using label conditioning. The model was trained on the FER-2013 dataset and evaluated over 300 epochs. Training results demonstrate stable adversarial loss convergence, indicating effective learning and generation capability.