Skin Detection Using Color Pixel Classification with Application to Face Detection: A Comparative Study

Abstract

This paper presents a comprehensive study of the pixel-based skin color detection techniques. Two main issues of the skin detection are the selection of the best color space and skin color pixel classification algorithm. A large set of XM2VTS face database is used to examine whether the selection of color space can enhance the compactness of the skin class and discriminability between skin and non-skin class in thirteen color spaces and six different skin color pixel classification algorithms. The results show that 1) the selection of the color space can improve the skin classification performance 2) the segmentation performance degrades only when chrominance information is used for classification 3) Bayesian classifier is found to perform better as compared to other classification algorithms. Piecewise linear decision boundary classifier algorithm outperforms all the other skin classification algorithms when it is used for images with good illumination conditions.

Advanced 3D movement analysis algorithms for robust functional capacity assessment

“…However, their major problem is the difficulty in empirically determining a color space and the relevant decision rules that provide a high recognition rate. Although the color of the skin can vary significantly, recent research shows that the main difference is in the intensity rather than chrominance [41]. Various color spaces are used to label the pixels as color skin pixels such as RGB [42], HSV [43] and YCbCr [44].…”

Segmentation of Football Video Broadcast

“…The first presents the global view of the field, the second usually displays the whole body of a player, the third one shows the above-waist view of a person and the last is associated with audience ( Fig. 14) [16]. The author decided to classify all shots into the following four categories: close-up shot -e.g.…”

“…The input frame and the mask as an output of the grass pixel ratio feature calculation. Skin color detection is done in RGB color space according to [16]. It may not be very accurate but sufficient for shot type classification.…”

A discriminative fusion framework for skin detection

“…Most of these methods have focused on the color-based classification of skin pixels. Various color spaces and different classification rules were investigated in order to present more robust detection methods [3], [4]. The main drawback of these techniques is that the decision is made for each pixel individually and the spacial dependencies between neighboring pixels are not taken into account.…”