Face Image Segmentation Using Boosted Grey Wolf Optimizer

Abstract

Image segmentation methods have received widespread attention in face image recognition, which can divide each pixel in the image into different regions and effectively distinguish the face region from the background for further recognition. Threshold segmentation, a common image segmentation method, suffers from the problem that the computational complexity shows exponential growth with the increase in the segmentation threshold level. Therefore, in order to improve the segmentation quality and obtain the segmentation thresholds more efficiently, a multi-threshold image segmentation framework based on a meta-heuristic optimization technique combined with Kapur’s entropy is proposed in this study. A meta-heuristic optimization method based on an improved grey wolf optimizer variant is proposed to optimize the 2D Kapur’s entropy of the greyscale and nonlocal mean 2D histograms generated by image computation. In order to verify the advancement of the method, experiments compared with the state-of-the-art method on IEEE CEC2020 and face image segmentation public dataset were conducted in this paper. The proposed method has achieved better results than other methods in various tests at 18 thresholds with an average feature similarity of 0.8792, an average structural similarity of 0.8532, and an average peak signal-to-noise ratio of 24.9 dB. It can be used as an effective tool for face segmentation.

A Grey Wolf Optimizer Algorithm for Multi-Objective Cumulative Capacitated Vehicle Routing Problem Considering Operation Time

“…Despite their advantages, genetic algorithms often face slow convergence, and particle swarm optimization may encounter local optimization pitfalls. The grey wolf optimizer (GWO) [ 48 ], introduced in 2014, stands out by simulating grey wolves’ social hierarchy and hunting strategies, offering rapid convergence and precise solutions to challenges ranging from face image segmentation [ 49 ] and the optimal reference tracking control problem [ 50 ] to brain tumor detection [ 51 ] and short-term power load forecasting [ 52 ].…”