Human Skin detection deals with the recognition of skin-colored pixels and regions in a given image. Skin color is often used in human skin detection because it is invariant to orientation and size and is fast to process. A new human skin detection algorithm is proposed in this paper. The three main parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue, Saturation, Value) and YCbCr (Luminance, Chrominance) color models. The objective of proposed algorithm is to improve the recognition of skin pixels in given images. The algorithm not only considers individual ranges of the three color parameters but also takes into ac- count combinational ranges which provide greater accuracy in recognizing the skin area in a given image.

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Human Skin Detection Using RGB, HSV and YCbCr Color Models

This paper presents a novel dynamic threshold approach to discriminate skin pixels and non-skin pixels in color images. Fixed decision boundaries (or fixed threshold) classification approaches are successfully applied to segment human skin. These fixed thresholds mostly failed in two situations as they only search for a certain skin color range: 1) any non-skin object may be classified as skin if non-skin objects's color values belong to fixed threshold range. 2) any true skin may be mistakenly classified as non-skin if that skin color values do not belong to fixed threshold range. Therefore in this paper, instead of predefined fixed thresholds, novel online learned dynamic thresholds are used to overcome the above drawbacks. The experimental results show that our method is robust in overcoming these drawbacks.

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A dynamic threshold approach for skin segmentation in color images

Personalized thermal comfort inference using RGB video images for distributed HVAC control

Steganography is the art of hiding the existence of data in another transmission medium to achieve secret communication. Steganography method used in this paper is based on biometrics. And the biometric feature used to implement Steganography is skin tone region of images (1). Here secret data is embedded within skin region of image that will provide an excellent secure location for data hiding. For this skin tone detection is performed using HSV (Hue, Saturation and Value) color space. Additionally secret data embedding is performed using frequency domain approach - DWT (Discrete Wavelet Transform), DWT outperforms than DCT (Discrete Cosine Transform). Secret data is hidden in one of the high frequency sub-band of DWT by tracing skin pixels in that sub-band. For data hiding two cases are considered, first is with cropping and other is without cropping. In both the cases different steps of data hiding are applied either by cropping an image interactively or without cropping i.e. on whole image. Both cases are compared and analyzed from different aspects. This is concluded that both cases offer enough security. Main feature of with cropping case is that this results into an enhanced security because cropped region works as a key at decoding side. Where as without cropping case uses embedding algorithm that preserves histogram of DWT coefficient after data embedding also by preventing histogram based attacks and leading to a more security. This study shows that by adopting an object oriented steganography mechanism, in the sense that, we track skin tone objects in image, we get a higher security. And simulation result shows that satisfactory PSNR (Peak-Signal-to-Noise Ratio) is also obtained.

/pdf/a-secure-skin-tone-based-steganography-using-wavelet-4rjacadgp0.pdf

A Secure Skin Tone based Steganography Using Wavelet Transform

This work is the part of vision based hand gesture recognition system for Natural Human Computer Interface. Hand tracking and segmentation are the primary steps for any hand gesture recognition system. The aim of this paper is to develop robust and efficient hand segmentation algorithm where three algorithms for hand segmentation using different color spaces with required morphological processing have were utilized. Hand tracking and segmentation algorithm (HTS) is found to be most efficient to handle the challenges of vision based system such as skin color detection, complex background removal and variable lighting condition. Noise may contain, sometime, in the segmented image due to dynamic background. An edge traversal algorithm was developed and applied on the segmented hand contour for removal of unwanted background noise.

Hand Segmentation Techniques to Hand Gesture Recognition for Natural Human Computer Interaction

Skin detection is the process of finding skin-colored pixels and regions in an image or a video. This process is typically used as a preprocessing step to find regions that potentially have human faces and limbs in images. Several computer vision approach es have been developed for skin detection. A skin detector typically transforms a given pix el into an appropriate color space and then use a skin classifier to label the pixel whether it is a ski n or a non-skin pixel. A skin classifier defines a decision boundary of the skin color class in the colo r space based on a training database of skin-colored pixels.

Zhouchen Lin

Papers

Skin Detection -a Short Tutorial