A Systematic Pelican Optimization based Weight Extreme Learning Machine Algorithm for Face Emotion Recognition

Abstract

Recognizing the facial expressions from the given input is one of the challenging and demanding tasks in recent days owing to the low-resolution images and different backgrounds. Also, the facial emotional/expression recognition system has gained a significant attention in the field of computer vision. The conventional works implemented a variety of machine learning algorithms for face emotion recognition, but higher computation costs, decreased reliability, redundant information, and increased computational time requirement are still only a few of the issues. In this case, an image face filtering method is employed in the beginning to produce a normalized output image with high contrast and quality, which is used to increase the classifier's overall rate of emotion recognition. Then, the novel pelican optimization algorithm (POA) is used to optimize the feature set in order to guarantee the success of classifier training and testing. Prior to emotion recognition, this technique is used to reduce the dimensionality of information. Based on the given face image's optimized attributes, the weight optimized extreme learning machine (WOELM) algorithm is used to reliably identify the emotions. By using well-known datasets like CK+ and MMI, a wide range of factors are taken into consideration for study in order to compare and evaluate the outcomes of the proposed POA-WOELM face emotion identification system. The obtained results reveal that the combination of POA-WOELM provides an increased 99% of emotion recognition accuracy to all kinds of emotions, with the sensitivity of 98.8% and specificity of 98.9%.

Swarm Space Hopping Algorithm: A Swarm-based Stochastic Optimizer Enriched with Half Space Hopping Search

“…In optimization studies, metaheuristic is a popular method that has been utilized in many sectors, especially engineering. Pelican optimization algorithm (POA) has been utilized in the machine learning-based face emotion recognition with the accuracy is 99% for various emotions [1]. Artificial bee colony (ABC) has been hybridized with Jaya algorithm to optimize the synthesis of linear antenna array with the optimized parameters are position, phase, and amplitude [2].…”