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explicitly defined skin regions in Cb Cr color space. Cb Cr color space was chosen in our work for many reasons; first, it contains no information about luminance which yields a more general skin color model; it has only two components which helps to speed up the calculations; the transformations from RGB color space into Cb Cr color space is done using simple and fast calculation. The main reason for using explicitly defined skin regions in building the skin detector is its speed in detecting the skin regions. To build the model, author collected samples of human skin from different races. For every pixel in the skin samples, the values of Cb and Cr are calculated. After the Cb Cr skin color model is built, it can be used for skin detection. The first step in skin detection is pixel based skin detection, where the skin detector tests every pixel of the input image and computes its Cb and Cr. If Cb and Cr values of the pixels satisfy the following condition, then this pixel is considered skin pixel. The ranges of values for skin color are,

 

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Pixels belonging to skin region exhibit similar Cb and Cr values. Furthermore, it has been shown that skin color model based on the Cb and Cr values can provide good coverage of different human races. The thresholds be chosen as [Cr1, Cr2] and [Cb1, Cb2], a pixel is classified to have skin tone if the values [Cr, Cb] fall within the thresholds The skin color distribution gives the face portion in the color image. This algorithm is also having the constraint that the image should be having only face as the skin region.

 

Skin color region is more effectively extracted. This is because Cb and Cr have some distinct color range for skin region. Thus the accuracy of this algorithm is quite good. The skin region from the image and from the skin detected image face is extracted by first extracting facial features and then drawing a bounding box around the face region with the help of facial features.After getting the skin region, facial features viz. eyes are extracted. The image obtained after applying skin color statistics is subjected to binarization i.e., it is transformed to gray-scale image and then to a binary image by applying suitable threshold. This is done to eliminate the hue and saturation values and consider only the luminance part. This luminance part is then transformed to binary image with some threshold because the features we want to consider further for face extraction are darker than the background colors. After thresholding, opening and closing operations are performed to remove noise. These are the morphological operations, opening operation is erosion followed by dilation to remove noise and closing operation is dilation followed by erosion which is done to remove holes. Since eyes lie in the upper half of the face, the lower part of face is removing in order to reduce search area. Following are the result of removal of lower part from face.

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