Nelson, Jill K.Kaur, Balvinder2012-09-17NO_RESTRIC2012-09-172012-09-17https://hdl.handle.net/1920/7925Image processing tools to detect human skin in visible band imagery have been well explored by many organizations, and approaches have been developed for many security and military applications. Visible cameras are limited to human skin detection during daylight or artificial illumination conditions, but the challenge of human skin detection during nighttime remains an ongoing research effort. The most challenging problems are to understand skin texture and to develop mathematical tools for discriminating skin texture from non-skin textures in images collected using a single thermal band. To solve this problem, a set of image processing algorithms have been designed and developed for generating the skin-texture feature set discriminating feature selection, and classification. First, Gray Level Co-occurrence Matrix (GLCM)-driven skin-texture features are generated based on the skin portions of the imagery. Principal Component Analysis (PCA) is then performed on the feature set to isolate the skin discriminating features. Then, PCA-reported skin discriminating features are employed to construct a fused image. The purpose of this fused image is to represent the skin pixels in terms of the skin-discriminating features and use this image for skin discrimination. In the last process, this fused image is used for skin and non-skin classification at the local level. For classification, three image processing approaches are adopted: 1) Adaptive optimized threshold with Least Mean Square algorithm, 2) Principal Component Analysis (PCA), and 3) Linear Discriminant Analysis (LDA). Results from all three classification techniques are analyzed for accuracy confidence levels. This research provides a generalized approach for human skin detection in thermal images, providing a noncontact, remote, and passive method for human skin detection in day or night imagery for security and military applications.enHuman facial skinPrincipal component analysisThermal imageryLeast mean square optimizationGray-level co-occurrence matrix (GCLM)Linear discriminant analysis (LDA)Thesis