Beginning with a color image,
the first stage is to transform it to a skin-likelihood image. This involves
transforming every pixel from RGB representation to chroma representation
and determining the likelihood value based on the equation given in the
previous section. The skin-likelihood image will be a gray-scale image
whose gray values represent the likelihood of the pixel belonging to skin.
A sample color image and its resulting skin-likelihood image are shown
in Figure 3. All skin regions (like the face, the hands and the
arms) were shown brighter than the non-skin region.
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(Right) The skin-likelihood image. |
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However, it is important to note that the detected regions may not necessarily correspond to skin. It is only reliable to conclude that the detected region have the same color as that of the skin. The important point here is that this process can reliably point out regions that do not have the color of the skin and such regions would not need to be considered anymore in the face finding process.
Since the skin regions are brighter than the other parts of the images, the skin regions can be segmented from the rest of the image through a thresholding process. To process different images of different people with different skin, a fixed threshold value is not possible to be found. Since people with different skins have different likelihood, an adaptive thresholding process is required to achieve the optimal threshold value for each run.
The adaptive thresholding is based on the observation that stepping the threshold value down may intuitively increase the segmented region. However, the increase in segmented region will gradually decrease (as percentage of skin regions detected approaches 100%), but will increase sharply when the threshold value is considerably too small that other non-skin regions get included. The threshold value at which the minimum increase in region size is observed while stepping down the threshold value will be the optimal threshold. In our program, the threshold value is decremented from 0.65 to 0.05 in steps of 0.1. If the minimum increase occurs when the threshold value was changed from 0.45 to 0.35, then the optimal threshold will be taken as 0.4.
Using this technique of adaptive
thresholding, many images yield good results; the skin-colored regions
are effectively segmented from the non-skin colored regions. The skin segmented
image of previous color image resulting from this technique shown in Figure
4. We present some more results using this skin detection technique
in Figures 5 and 6.
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It is clear from the results above
that not all detected skin regions contain faces. Some correspond to the
hands and arms and other exposed part of the body, while some corresponds
to objects with colors similar to those of the skin. Hence the second stage
of face finder will employ facial features to locate the face in all these
skin-like segments.
Next: Skin Regions Previous: Skin Color Model Contents: Face Detection