Automated Skin Detection using Fuzzy Logic based Pixel Matching Algorithm

Abstract

Objectives: Skin detection is the initial process of any automatic face recognition system. One of the fore most common approaches to detect the faces in facial pictures can be done by an automatic face detection method. In this paper, a new type of facial skin detection using the combination of Fuzzy and Pixel matching algorithms is proposed. Method/Statistical Analysis: The proposed system has three steps. Initially, the input facial color image is transformed in to a grey scale image and then, it is sharpened using a filter. Secondly, the pixels of the different types of facial grey scale images are computed. Finally, the computed pixels are compared with the original grey scale image based on the fuzzy rules. This process is done for the first pixel to the last pixel so that all the pixels which are present in the entire image can be included in the overall process. Findings: The algorithm is tested for different types of facial images for both the accuracy and the time taken to complete the skin detection process. The results of the experiments reveal that an accurate skin detection rate of 94.42% for the combination of fuzzy based pixel matching along with the dimension reduction methodology. Application/ Improvements: Future enhancements in this work are to propose an efficient algorithm to detect the facial images of group of faces present in a single image. Keywords: Dimension Reduction, Facial Image, Fuzzy Logic, Pixel Matching Algorithm, Skin Texture Analysis, Skin Detection