Segmentation of Facial Color Images based on Fuzzy Clustering Optimized by Grey Wolf and Whale Algorithms

Segmentation of facial color images is an essential step in the image processing and computer vision applications, such as face recognition, identity recognition, and analysis of facial plastic surgeries. One of the most important methods of facial image segmentation is clustering-based methods. The fuzzy c-means (FCM) clustering is an effective method in the image segmentation, but its sensitivity to initial values may cause that this algorithm fall and stuck into the local minima. To overcome this problem, the meta-heuristic algorithms, including Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA) have been used. Therefore, the main focus of this study is on the performance of the meta-heuristic algorithms in optimizing the FCM algorithm and their applications in the segmentation of facial color images. The objective function of the FCM algorithm is considered as a fitness function for meta-heuristic algorithms. This algorithm divides n vectors into C fuzzy groups and calculates the cluster center for each group. Also in this study, three color spaces (1) YCbCr, (2) YPbPr, and (3) YIQ have used as input data in optimization of the fitness function. After maximization of the membership function, segmentation of facial color images has been done on three database including, (1) Sahand University of Technology (SUT), (2) MR2, and (3) SCUTFBP. The result of segmentation show that convergence speed of the GWO and WOA methods is faster than other meta-heuristic algorithm, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Crow Search Algorithm (CSA), and Grasshopper Optimization Algorithm (GOA) and have a suitable performance in facial image segmentation.