Adaptive skin segmentation via feature-based face detection
| Autor: | Michael Taylor, Tim Morris |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Computer science
Scale-space segmentation Face detection Adaptive skin segmentation Computer vision Segmentation Electrical and Electronic Engineering Pixel business.industry Applied Mathematics Pattern recognition Filter (signal processing) Image segmentation Condensed Matter Physics Colour distribution modelling Computer Science Applications Electronic Optical and Magnetic Materials Feature (computer vision) Face (geometry) Artificial intelligence business Skin segmentation |
| Zdroj: | Taylor, M & Morris, T 2014, Adaptive skin segmentation via feature-based face detection . in Proceedings of SPIE-The International Society for Optical Engineering|Proc SPIE Int Soc Opt Eng . vol. 9139, SPIE, Real-Time Image and Video Processing 2014, Brussels, 1/07/14 . https://doi.org/10.1117/12.2052003 Real-Time Image and Video Processing |
| DOI: | 10.1117/12.2052003 |
| Popis: | Variations in illumination can have significant effects on the apparent colour of skin, which can be damaging to the efficacy of any colour-based segmentation approach. We attempt to overcome this issue by presenting a new adaptive approach, capable of generating skin colour models at run-time. Our approach adopts a Viola-Jones feature-based face detector, in a moderate-recall, high-precision configuration, to sample faces within an image, with an emphasis on avoiding potentially detrimental false positives. From these samples, we extract a set of pixels that are likely to be from skin regions, filter them according to their relative luma values in an attempt to eliminate typical non-skin facial features (eyes, mouths, nostrils, etc.), and hence establish a set of pixels that we can be confident represent skin. Using this representative set, we train a unimodal Gaussian function to model the skin colour in the given image in the normalised rg colour space - a combination of modelling approach and colour space that benefits us in a number of ways. A generated function can subsequently be applied to every pixel in the given image, and, hence, the probability that any given pixel represents skin can be determined. Segmentation of the skin, therefore, can be as simple as applying a binary threshold to the calculated probabilities. In this paper, we touch upon a number of existing approaches, describe the methods behind our new system, present the results of its application to arbitrary images of people with detectable faces, which we have found to be extremely encouraging, and investigate its potential to be used as part of real-time systems. © 2014 SPIE. |
| Databáze: | OpenAIRE |
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