Facial Expression Recognition Using Data Augmented Convolutional Neural Network

Facial expression recognition (FER) is a burgeoning field within computer vision and artificial intelligence, with significant implications for human-computer interaction and emotion analysis. Recent advancements in deep learning, particularly convolutional neural networks (CNN), have revolutionized facial expression recognition by enabling the automatic extraction of discriminative features from facial images. These breakthroughs have led to remarkable accuracy improvements in recognizing basic emotions such as happiness, anger, sadness, surprise, fear, and disgust. In this paper, we focus on the fusion of data augmentation techniques with CNN to improve facial expression recognition. By leveraging the power of deep learning, transfer learning, and augmenting the training data, we aim to enhance the model's ability to accurately classify facial expressions across a wide spectrum of emotions. The proposed approach is particularly beneficial in scenarios where the availability of labeled training data is limited. Experimental results on the CK+ dataset show improvements in accuracy compared to state-of-the-art methods.