A hybrid neural network using ICA and CGA for skin detection in RGB images

Skin color recognition is a useful and popular method in human-computer interaction and also in analyzing the content. In addition, the application programs for recognizing and detecting human body parts, faces, naked people, and retrieving individuals in multimedia databases all make use of skin recognition. Thus, finding a suitable method in order to segment the pixels of an image into different groups such as skin can be very important. Imperialist competitive algorithm (ICA) is a recently introduced evolutionary algorithm that showed a promising performance in some of the optimization problems. In this article, first the combined ICA-ANN, continuous genetic algorithm (CGA) and gradient descent algorithm were proposed and their performance was tested on images in RGB color spaces. In the proposed algorithms, a multilayer perceptron neural network manages the problem's constraints, and ICA and genetic algorithms search to calculate the best response than the gradient descent algorithm. The proposed skin classification algorithms perform directly on the RGB color space. The results clearly indicate that the proposed algorithm significantly improves the performance of an MLP neural network.