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processing foot room and headroom. On the other hand, the chrominance components Cb and Cr have excursions of +112 and offset of +128, producing a range from 16 to 240 inclusively.[1]

 

HSI Color Space

Since hue, saturation and intensity are three properties used to describe color, it seems logical that there be a corresponding color model, HSI. When using the HSI color space, It is not needed to know what percentage of blue or green is required to produce a color [1]. simply adjust the hue to get the color you wish. To change a deep red to pink, adjust the saturation. To make it darker or lighter, alter the intensity. Many applications use the HSI color model. Machine vision uses HSI color space in identifying the color of different objects.[1] Image processing applications such as histogram operations, intensity transformations and convolutions operate only on an intensity image. These operations are performed

 

with much ease on an image in the HIS color space. For the HSI being modeled with cylindrical coordinates, see Figure 2. The hue (H) is represented as the angle 0, varying from 0o to 360o. Saturation (S) corresponds to the radius, varying from 0to 1. Intensity (I) varies along the z axis with 0 being black and 1 being white.[1] When S = 0, color is a gray value of intensity 1. When S = 1, color is on the boundary of top cone base. The greater the saturation, the farther the color is from white/gray/black (depending on the intensity).[1] Adjusting the hue will vary the color from red at 0o, through green at 120o, blue at 240o, and back to red at 360o. When I = 0, the color is black and therefore H is undefined.[1] When S = 0, the color is grayscale. H is also undefined in this case .By adjusting I, a color can be made darker or lighter. By maintaining S = 1 and adjusting I, shades of that color are created.[1]

 

algorithms

In this section, I have described the three algorithms [1-3] which are based on RGB, YCbCr and HIS respectively. It has been stated that these three algorithms work very well under the condition that there is only one face is present in the image. In the implementation of the algorithms, there are three main steps viz. (1) Classify the skin region, (2) mask the skin region and (3) extract the face image.

 

1) Skin color based face detection in RGB color space

Crowley and Coutaz [19] said one of the simplest algorithms for detecting skin pixels is to use skin color algorithm. The perceived human color varies as a function of the relative direction to the illumination. The pixels for skin region can be detected using a normalized color histogram, and can be further normalized for changes in intensity on dividing 本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。