Enhanced Facial Expression Recognition via Deep Transfer Learning and Augmentation

Abstract

Facial Expression is one of the key parts of non-verbal communication. Facial Expression Recognition is the major application of surveillance, automation, health care, and education. Deep learning is important in different fields of computer vision due to its ability to process and analyze large volumes of data, extract features, and correctly classification of images. This research empirically evaluates the performance of a pre-trained model on augmented datasets for facial expression recognition. The study includes preprocessing techniques, data augmentation, and transfer learning using the ResNet50 model. The experiments are conducted on a dataset containing images of three facial expressions: happy, sad, and surprised. The results indicate significant improvements in accuracy as the dataset size and preprocessing techniques increase. In particular, Cubic Support Vector Machine (SVM) and Linear Cubic SVM consistently outperform other classifiers, achieving an impressive accuracy of 99.7% on the augmented dataset. The research demonstrates the potential of data augmentation and preprocessing in enhancing facial expression recognition systems.