Face Detection and Modeling for Recognition - Biometrics Research ...

size) to generate potential face candidates, within the entire image. The algorithm
first estimates and corrects the color bias based on a novel lighting compensation
technique. The corrected red, green, and blue color components are first converted
to the Y C b C r color space and then nonlinearly transformed in this color space (see
formulae in Appendix A). The skin-tone pixels are detected using an elliptical skin
model in the transformed space. The parametric ellipse corresponds to contours of
constant Mahalanobis distance under the assumption of the Gaussian distribution of
skin tone color. The detected skin-tone pixels are iteratively segmented using local
color variance into connected components which are then grouped into face candidates
based on both the spatial arrangement of these components (described in Appendix
B) and the similarity of their color [84]. Figure 3.1 shows the input color image, color
compensated image, skin regions, grouped skin regions, and face candidates obtained
from the face localization module. Each grouped skin region is assigned a pseudo
color and each face candidate is represented by a rectangle. Because multiple face
candidates (bounding rectangles) usually overlap, they can be fused based on the
percentage of overlapping areas. However, in spite of this postprocessing there are
still some false positives among face candidates.
It is inevitable that detected skin-tone regions will include some non-face regions
whose color is similar to the skin-tone. The facial feature detection module rejects
face candidate regions that do not contain any facial features such as eyes, mouth,
and face boundary. This module can detect multiple eye and mouth candidates. A
triangle is constructed from two eye candidates and one mouth candidate, and the
best-fitting enclosing ellipse of the triangle is constructed to approximate the face
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