Elsevier

Image and Vision Computing

Volume 18, Issue 1, 8 December 1999, Pages 63-75
Image and Vision Computing

Detecting human faces in color images

https://doi.org/10.1016/S0262-8856(99)00006-2Get rights and content

Abstract

A method for detecting human faces in color images is described that first separates skin regions from nonskin regions and then locates faces within skin regions. A chroma chart is prepared via a training process that contains the likelihoods of different colors representing the skin. Using the chroma chart, a color image is transformed into a gray scale image in such a way that the gray value at a pixel shows the likelihood of the pixel representing the skin. An obtained gray scale image is then segmented to skin and nonskin regions, and model faces representing front- and side-view faces are used in a template-matching process to detect faces within skin regions. The false-positive and false-negative errors of the proposed face-detection method on color images of size 300×220 and containing four or fewer faces are 0.04 or less.

Section snippets

Introduction and background

Indexing and retrieval of video images containing human activities require automatic detection of humans and, in particular, the human face in the images. Techniques that recognize faces or analyze facial expressions also require knowledge about the locations of faces in images. In this article a new method for detecting faces in color images is presented.

The majority of existing face-detection methods use image gray values to detect faces [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]

Preparing the chroma chart

Color charts have been used in face detection with considerable success [12], [14], [15]. Past color charts, however, have been binary: colors have been grouped to either skin or nonskin. In our model, any color can represent the skin, but with a different likelihood.

Our color chart will have two components, representing the a and b values in the 1976 CIE Lab color space [19]. Therefore, we refer to our color chart as the chroma chart. As mentioned earlier, we do not use the luminance component

Detecting skin-color regions

To identify skin-color regions in an image, we first transform a color image into a skin-likelihood image. This involves transforming the RGB values at a pixel (x,y) to (L,a,b) values, reading the likelihood from entry (a,b) in the chroma chart, and saving it at location (x,y) in the image. When this process is repeated for all pixels in the image, an image will be obtained whose gray values represent the likelihoods of pixels belonging to the skin.

Using the color image of Fig. 2(a) and the

Detecting faces in skin regions

The main objective in detecting skin regions in an image is to reduce the search space for the faces. Naturally, faces are in skin regions, and there is no point looking for them in nonskin regions. Facial features that do not have the color of the skin, such as the eyes or the mouth, appear as small regions within skin regions. These features can be detected and used to generate hypotheses about the faces, and their presence can be verified by template matching.

Since features such as the eyes

Results and discussion

Fig. 6(a) shows the best-match pose of the front-view model of Fig. 3(c) in the luminance component of image of Fig. 2(a). the cross-correlation coefficient in this matching is 0.57. Our program identifies a detected face by placing a square around it. The center of the square is at the tip of the nose of the model, and the two sides of the square are parallel to the major axis of the face model when matching the image (see Fig. 6(b)). The brightness of the square is proportional to the match

Summary and conclusions

Indexing and retrieval of images containing human activities require the ability to detect humans and, in particular, human faces in images. This article presented a new method for detecting human faces in color images, which first determines the skin-color regions and then locates faces within those regions. A chroma chart was prepared through a learning process that contains the likelihoods of different colors representing the skin. The chroma chart was then used to distinguish skin regions

Acknowledgements

The authors would like to thank David Kriegman and Peter Belhumeur of Yale University and Bernard Achermann of the University of Bern for making their face databases available to them. The support of the National Science Foundation under grant IRI-9529045 is also greatly appreciated.

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