Improving the performance of skin segmentation in quasi-skin regions via multiple classifier system

This paper presents a skin segmentation method based on multiple classifier system strategy in order to improve the performance of classification especially in quasi-skin regions. Quasi-skin regions in digital images are non-skin patches which have characteristics like the human skin and are known as a basic origin of misclassification error in skin segmentation. To cope with this problem, we have designed an algorithmic architecture by combining four prominent classifiers to construct a synergy to conceal their weaknesses and amplify their strengths. Participant classifiers in our approach include cellular learning automaton, likelihood, Gaussian and Support Vector Machines in which decision making performs via a conditional voting step. The accuracy and specificity were employed to evaluate the performance. Experiments on a collected test-set database including 142 challenging images demonstrate that the proposed skin detector is able to improve the accuracy and specificity up to 1.92% and 0.83%, respectively, than the best of individual classifier.