Feature Extraction Techniques for Facial Expression Recognition (FER)

Abstract

Facial expression recognition (FER) is a significant area of study in computer vision and affective computing. In numerous applications, such as human-computer interaction, emotion detection, and behavior analysis. Feature extraction is a crucial stage in facial expression recognition systems, as it involves extracting pertinent information from facial images in order to accurately represent various facial expressions. The purpose of this paper is to investigate and compare the various feature extraction techniques used in facial expression recognition, as well as their merits, limitations, and impact on overall system performance. Using benchmark datasets and performance metrics, the evaluation provides insight into the efficacy of various feature extraction methods. In this study, we propose a method for facial expression recognition that integrates deep learning, Principal Component Analysis (PCA), and Gray Level Co-occurrence Matrix (GLCM). Use 1D-CNN for classification.

Keywords

• Feature extraction, Handcrafted method, Deep learning method, fisher Vector Encoding