Date of Publication :1st June 2023

Abstract: Facial Expression Recognition (FER) is an important task in computer vision that has many potential applications in fields such as psychology, medicine, security, and entertainment. However, FER is a challenging task due to the complex and dynamic nature of facial expressions, and the variability in lighting, pose, and other environmental factors. In this article, we provide an overview of the key challenges associated with FER, including data collection and preprocessing, feature extraction, classification, and real-world deployment. We also review some of the current approaches and techniques that have been developed to address these challenges, such as deep learning models, data augmentation, and transfer learning. The process of FER typically involves several steps, including face detection, face alignment, feature extraction, and classification. For each of these steps, we discuss some of the most widely used techniques and methods, such as Viola-Jones for face detection, Local Binary Patterns (LBP) and Gabor wavelets for feature extraction, and Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for classification. In terms of applications, we describe some of the key areas where FER can be applied, including emotion analysis, human-computer interaction, and security and surveillance. We also discuss the current approaches and techniques that have been developed for FER in each of these domains, and highlight some of the key challenges and limitations associated with each area. Fnally, we review some of the most widely used FER datasets, including the Cohn-Kanade, MMI, and AffectNet datasets, and describe the key features and limitations of each of these datasets. We also discuss some of the current approaches and techniques that have been developed for data augmentation and transfer learning, which can help to improve the performance of FER models when training data is limited. The insights and recommendations presented in this article can help guide the development of more accurate and efficient FER systems that can be applied in a range of real-world scenarios.

Reference :