Optimal Selection of Features for Human Emotion Identification from Face Images
Keywords:
Facial Emotion Recognition, Optimization Algorithms, Feature Reduction, Particle Swarm Optimization, Genetic Algorithms.Abstract
Facial expressions play a powerful role in human communication, serving as a highly effective non-verbal means of conveying emotions during social interactions. While humans naturally excel at interpreting these emotions, teaching machines to recognize facial expressions is a daunting task. The objective of this study is to develop a system capable of replicating human visual perception by harnessing artificial intelligence techniques to analyze input images. Facial expression recognition systems are gaining widespread application in various domains like gaming and the internet, significantly enhancing the efficiency of robots in sectors including military, healthcare, and manufacturing. Nonetheless, the abundance of features in image descriptors poses a substantial challenge for facial emotion recognition systems. Despite numerous attempts to simplify feature complexity, the intricate and diverse nature of facial expressions makes the selection of discriminative features a complex undertaking. In this paper, proposed an effective feature selection method designed to pinpoint and choose informative features from high-dimensional data, with the explicit goal of optimizing classification accuracy. Our approach leverages Particle Swarm Optimization (PSO) to identify valuable feature combinations for classification, using the accuracy determined by the K - Nearest Neighbour (KNN) and Linear Discriminant Analysis classifier (LDA) to assess fitness within the PSO algorithm.
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