CG-FER : A Hybrid CNN and GAN based Facial Expression Recognition

Facial expression recognition (FER) has come to be of utmost importance for any number of applications from human-computer interfaces to security systems and behavioral analytics. However, the subtle variations of facial features prove challenging to analyze accurately since labeling training data is generally scarce. This article proposes a hybrid approach that combines Generative Adversarial Networks (GAN) and Convolutional Neural Networks (CNN) to increase the precision and robustness of the facial expression classification. CNNs extract Accordingly, they provide detailed semantic representations from images, which thus enable accurate categorization of emotions. Simultaneously, GANs create synthetic likeness for complementing the natural samples in such a manner that it increases the potential size of the training corpus using data augmentation. This dual-pronged strategy enhances model generalization capabilities, overcoming restrictions imposed by scarce labelled resources alone. Even though computer-assisted facial expression analysis helps address issues like class imbalance, recognition scores still largely depend on understanding the complexities of emotions involving human beings. The simulated images from generative adversarial networks augment facial expression recognition training sets. However, capturing the fineness and complexities that define human emotions, it is essential to combine convolutional neural networks with generative techniques. Our experiments demonstrate this hybrid approach improves performance across 7 categories by leveraging both artificial and real examples. In real-world use, where data limitations and intense reactions challenge classification, this two-pronged method shows promise for enhancing facial expression recognition systems to better understand the varied and vibrant emotions we all experience.