A Comprehensive Survey on Human Skin Detection

Abstract

Human Skin detection is one of the most widely used algorithms in vision literature which has been numerously exploited both directly and indirectly in multifarious applications. This scope has received a great deal of attention specifically in face analysis and human detection/tracking/recognition systems. As regards, there are several challenges mainly emanating from nonlinear illumination, camera characteristics, imaging conditions, and intra-personal features. During last twenty years, researchers have been struggling to overcome these challenges resulting in publishing hundreds of papers. The aim of this paper is to survey applications, color spaces, methods and their performances, compensation techniques and benchmarking datasets on human skin detection topic, covering the related researches within more than last two decades. In this paper, different difficulties and challenges involved in the task of finding skin pixels are discussed. Skin segmentation algorithms are mainly based on color information; an in-depth discussion on effectiveness of disparate color spaces is elucidated. In addition, using standard evaluation metrics and datasets make the comparison of methods both possible and reasonable. These databases and metrics are investigated and suggested for future studies. Reviewing most existing techniques not only will ease future studies, but it will also result in developing better methods. These methods are classified and illustrated in detail. Variety of applications in which skin detection has been either fully or partially used is also provided.

An Efficient Scheme of Deep Convolution Neural Network for Multi View Face Detection

“…Neural network is being used historically for face detection [15,16]. Yang et al [17] and Mahmoodi et al [18] provides detailed face detection survey. Vaillant have applied neural networks for detecting faces in uncluttered images.…”

An Adaptive Skin Detection Approach of Face Images with Unequal Luminance, Color Excursion, and Background Interference

“…As mentioned in the second section, in addition to the most common RGB model, common color models are HSB, YCbCr, XYZ, CMYK, Lab, and so on [4][5][6][7][8][9][10][11][12][13][14][15]. HSB is often used as a temporary model in image processing.…”

“…(14): Based on the established L'a'b' 3D skin model according to Eq. (14), the skin probability can be calculated by Eq. (15) , it is plain that the CbCr ellipse model combined with the pretreatment technology proposed in this paper can describe skin tone better than the traditional CbCr ellipse model, while the L'a'b' 3D model combined with the proposed pretreatment technology in this paper can improve the ability of skin-tone description.…”

“…Face detection approaches based on skin models are typically approaches based on a number of key features. They generally construct a skin model [4][5][6][7][8][9][10][11][12][13][14][15] using the clustering distribution of skin tones in the color space in order to detect skin. These approaches calculate quickly and are not easily affected by factors such as expression, posture and rotation but are easily affected by unequal luminance, different color excursions, and background interference [16][17][18].…”

Low Cost Skin Segmentation Scheme in Videos Using Two Alternative Methods for Dynamic Hand Gesture Detection Method

“…This is because utilising skin colour threshold approach can easily and simply segment the skin features including hand skin from the background to be used within dynamic hand gesture segmentation methods [6]. However, skin colour threshold approach is limited by illumination variations, as well as the interference with similar skin colour objects from the background [7]. Therefore, many researchers have proposed approaches depending on a set of conditions derived from the skin spot in 2D or 3D colour spaces.…”