Texture Patch Dataset.zip

This study uses an in-house two-class dataset comprising 1000 texture images, 500 of which denote human skin texture and the rest represent non-skin textures with high degree of similarity to human skin. These skin texture images were taken by our research team using a DSLR camera to maintain the fine skin texture patterns. Images were captured from various ethnic groups and skin color tones to avoid biasness toward any ethnic groups or skin color. Since different human body parts have different skin texture characteristics, texture images in our dataset were collected from various body parts such as face, leg, core, arms with relatively equal amount of contribution in dataset. In order to better simulate the skin detection challenges in real world and improve the robustness and reliability of our experiments, skin texture images were collected in various scales, direction and lighting conditions. The remaining 500 texture images which denote non-skin textures with high degree of similarity to human skin were collected from various online image repositories. These images, represent the texture of wooden surfaces, sand and other objects such as rugs, animal furs and fabric which are highly similar to actual skin texture, color and hue. The dataset which prepared in this study simulates challenging real-world scenarios to evaluate and compare texture analysis techniques performance in challenging conditions. All texture patches were captured and stored in uncompressed Tagged Image File Format (TIFF) to avoid any alteration or compromise in actual texture patterns. Moreover, any kind of color alternation or image enhancement were avoided. All texture images were manually resized to 150x150 dimension to equalize the amount of contribution of each image in the model.