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

normalized red-green (r-g) space [156] is not the best choice for face detection [157],
[158]. Based on Terrillon et al.’s [157] comparison of nine different color spaces for
face detection, the tint-saturation-luma (TSL) space provides the best results for
two kinds of Gaussian density models (unimodal and mixture of Gaussian densities).
We adopt the Y C b C r space since it is perceptually uniform [155], is widely used in
video compression standards (e.g., MPEG and JPEG) [21], and it is similar to the
TSL space in terms of the separation of luminance and chrominance as well as the
compactness of the skin cluster. Many research studies assume that the chrominance
components of the skin-tone color are independent of the luminance component [159],
[160], [158], [90]. However, in practice, the skin-tone color is nonlinearly dependent
on luminance. In order to demonstrate the luma dependency of skin-tone color, we
manually collected training samples of skin patches (853, 571 pixels) from 9 subjects
(137 images) in the Heinrich-Hertz-Institute (HHI) image database [15]. These pixels
form an elongated cluster that shrinks at high and low luma in the Y C b C r space,
shown in Fig. 3.3(a). Detecting skin tone based on the cluster of training samples in
the C b -C r subspace, shown in Fig. 3.3(b), results in many false positives. If we base
the detection on the cluster in the (C b /Y )-(C r /Y ) subspace, shown in Fig. 3.3(c), then
many false negatives result. The dependency of skin tone color on luma is also present
in the normalized rgY space in Fig. 3.4(a), the perceptually uniform CIE xyY space
in Fig. 3.4(c), and the HSV spaces in Fig. 3.4(e). The 3D cluster shape changes at
different luma values, although it looks compact in the 2D projection subspaces, in
Figs. 3.4(b), 3.4(d) and 3.4(f).
To deal with the skin-tone color dependence on luminance, we nonlinearly trans-
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