Classification of skin pixels in images : Using feature recognition and threshold segmentation
Abstract: The purpose of this report is to investigate and answer the research question: How can current skin segmentation thresholding methods be improved in terms of precision, accuracy, and efficiency by using feature recognition, pre- and post-processing? In this work, a novel algorithm is presented for classification of skin pixels in images. Different pre-processing methods were evaluated to improve the overall performance of the algorithm. Mainly, the methods of image smoothing, and histogram equalization were tested. Using a Gaussian kernel and contrast limited adaptive histogram equalization (CLAHE) was found to give the best result. A face recognition technique based on learned face features were used to identify a skin color range for each image. Threshold segmentation was then used, based on the obtained skin color range, to extract a skin map for each image. The skin maps were improved by testing a morphology method called closing and by using contour detection for an elimination of large false skin structures within skin regions. The skin maps were then evaluated by calculating the precision, recall, accuracy, and f-measure using a ground truth dataset called Pratheepan. This novel approach was compared to previous work in the field and obtained a considerable higher result. Thus, the algorithm is an improvement compared to previous work within the field.
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