Human Skin Detection Using RGB, HSV and YCbCr Color Models

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.Comment: ICCASP/ICMMD-2016. Published by Atlantic Press. Part of series: AISR ISBN: 978-94-6252-305-0 ISSN: 1951-685

Region-based Skin Color Detection.

Skin color provides a powerful cue for complex computer vision applications. Although skin color detection has been an active research area for decades, the mainstream technology is based on the individual pixels. This paper presents a new region-based technique for skin color detection which outperforms the current state-of-the-art pixel-based skin color detection method on the popular Compaq dataset (Jones and Rehg, 2002). Color and spatial distance based clustering technique is used to extract the regions from the images, also known as superpixels. In the first step, our technique uses the state-of-the-art non-parametric pixel-based skin color classifier (Jones and Rehg, 2002) which we call the basic skin color classifier. The pixel-based skin color evidence is then aggregated to classify the superpixels. Finally, the Conditional Random Field (CRF) is applied to further improve the results. As CRF operates over superpixels, the computational overhead is minimal. Our technique achieves 91.17% true positive rate with 13.12% false negative rate on the Compaq dataset tested over approximately 14,000 web images

Fair comparison of skin detection approaches on publicly available datasets

Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different datasets. The major contributions of this work are an exhaustive literature review of skin color detection approaches, a framework to evaluate and combine different skin detector approaches, whose source code is made freely available for future research, and an extensive experimental comparison among several recent methods which have also been used to define an ensemble that works well in many different problems. Experiments are carried out in 10 different datasets including more than 10000 labelled images: experimental results confirm that the best method here proposed obtains a very good performance with respect to other stand-alone approaches, without requiring ad hoc parameter tuning. A MATLAB version of the framework for testing and of the methods proposed in this paper will be freely available from https://github.com/LorisNann

An Innovative Skin Detection Approach Using Color Based Image Retrieval Technique

From The late 90th, "Skin Detection" becomes one of the major problems in image processing. If "Skin Detection" will be done in high accuracy, it can be used in many cases as face recognition, Human Tracking and etc. Until now so many methods were presented for solving this problem. In most of these methods, color space was used to extract feature vector for classifying pixels, but the most of them have not good accuracy in detecting types of skin. The proposed approach in this paper is based on "Color based image retrieval" (CBIR) technique. In this method, first by means of CBIR method and image tiling and considering the relation between pixel and its neighbors, a feature vector would be defined and then with using a training step, detecting the skin in the test stage. The result shows that the presenting approach, in addition to its high accuracy in detecting type of skin, has no sensitivity to illumination intensity and moving face orientation.Comment: 9 Pages, 4 Figure

A hybrid technique for face detection in color images

In this paper, a hybrid technique for face detection in color images is presented. The proposed technique combines three analysis models, namely skin detection, automatic eye localization, and appearance-based face/nonface classification. Using a robust histogram-based skin detection model, skin-like pixels are first identified in the RGB color space. Based on this, face bounding-boxes are extracted from the image. On detecting a face bounding-box, approximate positions of the candidate mouth feature points are identified using the redness property of image pixels. A region-based eye localization step, based on the detected mouth feature points, is then applied to face bounding-boxes to locate possible eye feature points in the image. Based on the distance between the detected eye feature points, face/non-face classification is performed over a normalized search area using the Bayesian discriminating feature (BDF) analysis method. Some subjective evaluation results are presented on images taken using digital cameras and a Webcam, representing both indoor and outdoor scenes

Infrared imaging spectroscopy of skin cancer lesions

Skin cancer is a disease of the twenty-first century since, unfortunately, being tan is associated to be healthy and good looking. UV radiation produces one of the most aggressive kinds of skin cancer: melanoma; once the damage is done there is no other solution that a rapid and effective diagnosis. Clinical examination and biopsies have shown to be slow and costly in many ways, so the possibility of getting a non-invasive optical detection of skin melanomas became a hot topic in biophotonics. In this context, multispectral imaging systems have approached the problem, but none of them worked inside the infrared range. Hence, this work has been proposed as an interesting, long-term project to further investigate about the possibilities of infrared imaging spectroscopy for the early detection of skin cancer through the development of such a system based on an InGaAs camera

Elaboration of integrated microelectrodes for the detection of antioxidant species

(Pt–Pt–Ag/AgCl) and (Au–Pt–Ag/AgCl) electrochemical microcells (ElecCell) were developed for the detection of redox species by cyclic voltammetry. A special emphasis was placed on the SU-8 waferlevel passivation process in order to optimize the electrochemical properties of the different “thin film” metallic layers, i.e. gold or platinum for the working electrode, platinum for the counter electrode and silver/silver chloride for the reference electrode. (Au–Pt–Ag/AgCl) microcells were applied for the detection of antioxidant species such as ascorbic and uric acids in phosphate buffer solution, evidencing high sensitivity but low selectivity. Works were extended to skin analysis, demonstrating that a good electrical contact with the skin hydrolipidic film allowed the effective evaluation of the skin global antioxidant capacity