Facial Skin Segmentation Using Bacterial Foraging Optimization Algorithm

Abstract

Background: Nowadays, analyzing human facial image has gained ever-increasing importance due to its various applications. Image segmentation is required as a very important and fundamental operation for significant analysis and interpretation of images. Methods: Among the segmentation methods, image thresholding technique is one of the most well-known methods due to its simplicity, robustness and high precision. Thresholding based on optimization of the objective function is among the best methods. Numerous methods exist for the optimization process and Bacterial Foraging Optimization is among the most efficient and novel ones. Using this method, optimal threshold is extracted and then segmentation of facial skin is performed. In the proposed method, first, the color facial image is converted from RGB color space to IHLS color space, because IHLS has a great mapping of the skin color. To perform thresholding, entropy-based method is applied. In order to find the optimum threshold, BFO is used.Results: In order to analyze the proposed algorithm, color images of the database of Sahand University of Technology of Tabriz, Iran were used. Then, using Otsu and Kapur methods, thresholding was performed. In order to have a better understanding from the proposed algorithm, genetic algorithm is also used for finding the optimum threshold. Conclusions: The proposed method shows the better results than other thresholding methods. These results include misclassification error accuracy (88%), non-uniformity accuracy (89%) and the accuracy of region’s area error (89%).