Bachelor thesis in computer science, written at University of Milano-Bicocca.
An interactive overview of the thesis is available at 123mpozzi.github.io/skin-detection-io/
Skin detection is the process of discriminating skin and non-skin pixels in an arbitrary image and represents an intermediate step in several image processing tasks, such as facial analysis and biomedical segmentation. Different approaches have been presented in the literature, but a comparison is diffcult to perform due to multiple datasets and varying performance measurements. In this work, the datasets and the state-of-the-art approaches are reviewed and categorized using a new proposed taxonomy. Three different representative skin detector methods of the state of the art are selected and thoroughly analyzed. This approaches are then evaluated on three different state of the art datasets and skin tones sub-datasets using multiple metrics. The evaluation is performed on single and cross dataset scenario to highlight key differences between methods, reporting also the inference time. Finally, the results are organized into multiple tables, using the related figures as an assistance tool to support the discussion. Experimental results demonstrate the strength and weaknesses of each approach, and the need to involve multiple metrics for a fair assessment of the method’s aspects.
The code used in the thesis is split into separate modules:
| Module | Description |
|---|---|
| nbrancati | Thresholding approach |
| skin-statistical | Statistical approach |
| skinny | Deep learning approach |
| skinny-bench | Deep learning approach inference times |
| thesis-metrics | Thesis performance tables |