Image analysis – Color image processing
Reexamination Certificate
2000-10-20
2004-03-23
Johns, Andrew W. (Department: 2621)
Image analysis
Color image processing
C382S167000, C382S275000, C358S515000, C358S518000, C358S520000, C358S523000
Reexamination Certificate
active
06711286
ABSTRACT:
FIELD OF THE INVENTION
The current invention relates to the field of digital detection for skin color pixels in a digital image, and in particular to the field of hair-skin color pixel separation in a digital image.
BACKGROUND OF THE INVENTION
Skin color detection is adopted as a preliminary step in automatic redeye detection and correction of consumer images for real-time use (see commonly assigned U.S. Ser. No. 08/919,560 filed Aug. 29, 1997 entitled “A Computer Program Product for Redeye Detection”). In this redeye detection application, skin color areas are first located and subsequent steps are employed to determine if the red dots within the skin color areas are true red eyes. The success of redeye detection largely depends on the success of having clean face skin color regions identified. Previous experiences tell that it is particularly difficult to obtain clean face regions in color images with the presence of blond hairs.
The algorithm designed in the aforementioned redeye detection application is used for locating and correcting redeye defects in consumer images without user intervention such as ROI (region-of-interest) selection. The major goal of the redeye detection algorithm is to detect redeye defects with a minimum number of false positives and within a minimum execution time without sacrificing performance. For this reason, face-region (with most of the hair regions eliminated) detection is performed so that unnecessary processing is not performed on red-dot candidates that are not located in detected face regions. The easiest and fastest way for face-region localization is the use of skin-color pixel detection that requires only pixel operations. It has been shown that difficulties arise when dealing with images having faces associated with blond hairs. In these cases, the skin-color detection process fails to produce satisfactory or desired results that would assist in the redeye detection procedure.
FIG. 1
displays an example picture
500
that causes problems. In
FIG. 1
, objects
504
,
505
,
508
,
509
, and
510
are non-skin objects;
501
and
506
are blond hairs;
502
,
503
, and
507
are skin regions. In
FIG. 2
, picture
600
shows the example result of conventional skin detection algorithms. Clearly, the skin-color detection process does not separate the hairs from the face (see object
601
) and therefore the subsequent redeye detection process will take the whole head plus the body as the face region and no redeye defects will be corrected.
There have been many publications recently addressing skin color detection for face recognition in color image processing, but only a few of them concern the issue of hair-face skin pixel identification. For instance, in Wu et al. (H. Wu, Q. Chen, and M. Yachida, “Face Detection From Color Images Using a Fuzzy Pattern Matching Method,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 6, pp. 557-563, 1999), a hair model is used to assist face detection. The RGB color information in an image is first converted to CIE's XYZ color representation through a linear transformation resulting in one luminance component Y and two chromaticity components x=X/(X+Y+Z) and y=Y/(X+Y+Z). Then the two chromaticity components, x and y, are furthered converted to another space through a non-linear transformation, resulting in two new color components, u and v, that are perceptually uniformly-distributed in the new color space. The hair model is a function of three variables: the luminance Y and the chromaticities, u and v. Noticeably, this hair model works mainly for Asian faces with dark hairs. Moreover, the conversion from RGB to the corresponding CIE tristimulus values requires the knowledge of the color calibration that varies from imaging device to device.
There are a number of color spaces currently used by researchers for color image processing as described below.
Psychological space—Some researchers believe that the RGB basis is not a particularly good one to explain the perception of colors. Alternatively, a transformed non-RGB space, or a psychological space, is well accepted to describe ‘colors’. It is compatible to human color perception. This non-RGB space is composed of three components, hue (H), saturation (S) and brightness value (V). Instead of using three values (R,G,B) to distinguish color objects, a single component, H, is used to label a color pixel in this transformed space.
CIELab space-CIE in 1976 recommended the CIELab formula for color measurement. It is designed that colors in the CIELab space are perceptually more uniformly spread than are colors in RGB and psychological (e.g. HSV) spaces. Therefore, using the CIELab space enables the use of a fixed color distance in decision making over a wide range of colors.
Lst space—The Lst space is traditionally called T-space in graphic applications and is constructed with log exposures. L is the luminance component, and s and t are the two chrominance components. It is shown that the statistical distribution of color signals is more symmetrical in Lst space than in linear space.
YC
R
C
B
space—The YC
R
C
B
space is designed for television broadcasting signal conversion. Y is the luminance component, C
R
and C
B
are two chrominance components. Researchers working in video images prefer using this space.
Generalized R-G-B Space (gRGB)—This is also called normalized R-G-B space. This space is transformed from the native R-G-B space by normalizing each of the three elements of the original R-G-B by the summation of the three original elements. The resultant three new elements are linearly dependent so that only two elements are needed to effectively form a new space that is collapsed from three dimensions to two dimensions. So, it is also called a collapsed R-G-B space in some articles. This space does not provide an explicit luminance component like the other spaces. This generalization process reduces the illuminant effects on chromaticity components.
So far, there are no conclusive data showing that any one of the above color spaces is overwhelmingly superior to any others in terms of skin-color detection performance. The skin-color detector performance, rather, mostly depends on the structure of the detector itself. The reality is that space transformation from RGB to another color domain does not change the skin-pixel and non-skin-pixel distribution overlap in the original RGB space. This skin-pixel and non-skin-pixel distribution overlap is the major cause of FP (false positive) in skin-color detection. The selection of color space largely depends on designers' preference, practical use effectiveness, and model complexity.
TABLE 1
Space transformation computation complexity (pixel-wise)
HSV
Lab
Lst
YC
R
C
B
gRGB
Addition
3.17
9
5
9
2
Multiplication
1.5
32
4
9
2
Logical Operation
5
1
Note: for HSV, only H is considered; gRGB stands for generalized RGB
Table 1 illustrates the computation expense for different transformation operations. Among them, gRGB transformation has the lowest computation expense. This is one of the advantages that gRGB transformation provides.
What is therefore needed is a way to provide an efficient skin-color detection method with which desirable clean face regions can be obtained with very low computation complexity prior to further image processing for applications such as the aforementioned redeye detection in the presence of blond hairs. The present invention describes a mechanism that overcomes the difficulty of separating blond hair pixels from skin color pixels by fusing two color detection strategies that work in generalized RGB (gRGB) space and the hue space respectively.
SUMMARY OF THE INVENTION
It is an objective of the present invention to automatically detect the skin color region in a digital color image.
It is a further object of the present invention to produce a clean skin color image in the presence of blond hairs.
The present invention is directed to overcoming one or more of the problems set forth above. Briefly summarized, accord
Chen Shoupu
Ray Lawrence A.
Alavi Amir
Eastman Kodak Company
Woods David M.
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