Skin Segmentation Using YUV and RGB Color Spaces

Abstract

Abstract-Skin detection is used in many applications, such as face recognition, hand tracking, and human-computer interaction. There are many skin color detection algorithms that are used to extract human skin color regions that are based on the thresholding technique since it is simple and fast for computation. The efficiency of each color space depends on its robustness to the change in lighting and the ability to distinguish skin color pixels in images that have a complex background. For more accurate skin detection, we are proposing a new threshold based on RGB and YUV color spaces. The proposed approach starts by converting the RGB color space to the YUV color model. Then it separates the Y channel, which represents the intensity of the color model from the U and V channels to eliminate the effects of luminance. After that the threshold values are selected based on the testing of the boundary of skin colors with the help of the color histogram. Finally, the threshold was applied to the input image to extract skin parts. The detected skin regions were quantitatively compared to the actual skin parts in the input images to measure the accuracy and to compare the results of our threshold to the results of other's thresholds to prove the efficiency of our approach. The results of the experiment show that the proposed threshold is more robust in terms of dealing with the complex background and light conditions than others.

A New Efficient Approach to Detect Skin in Color Image Using Bayesian Classifier and Connected Component Algorithm

“…[6][7][8][9][10][11][12][13] were successfully applied to skin detection problem. Some studies concluded that the skin detection performance can be improved when using two different color spaces together [14][15][16]. According to the above studies, this paper also applies a combined color space, RGBUV, which was proven to be effective by [14], to skin detection problem.…”

“…Some studies concluded that the skin detection performance can be improved when using two different color spaces together [14][15][16]. According to the above studies, this paper also applies a combined color space, RGBUV, which was proven to be effective by [14], to skin detection problem. For (ii), to classify whether a pixel is skin or not, the majority of previous studies usually focused on two groups of methods: the thresholding and the machine learning methods.…”

“…If the color of a pixel falls into the "skin" region, it is classified as "skin" and vice versa. Some studies that applied the thresholding method to skin detection can be referred to as in [14,[17][18][19][20]. In short, the thresholding method gains an advantage because it is a very basic and understandable method; however, it is mainly based on subjective experience and has low performance when the thresholds are incorrectly tuned [1,21].…”

“…However, the Bayesian classifier, as well as other methods, still suffers from low performance, especially the high false detection rate (the percentage of nonskin classified as skin). The main causes leading to such low performance and high false detection rate are the confusing background, the noise like skin pixels, and the various conditions of skin color with respect to different ages, sex, races, and body parts [14,30]. Figure 1 shows the distribution of "skin" and "nonskin" pixels in a particular image, using U and V color channels.…”

A hybrid skin color detection using HSV and YCgCr color space for face detection

“…A combination of YUV and RGB is used in [3] for skin detection. Other related works using color space combination are also reported in [3].…”