scholarly journals Color Space to Detect Skin Image: The Procedure and Implication

2017 ◽  
Vol 4 (2) ◽  
pp. 143-149 ◽  
Author(s):  
Sukmawati Nur Endah ◽  
Retno Kusumaningrum ◽  
Helmie Arif Wibawa
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Image Data ◽  
Skin Detection ◽  
Test Results ◽  
Color Spaces ◽  
Color Feature

Skin detection is one of the processes to detect the presence of pornographic elements in an image. The most suitable feature for skin detection is the color feature. To be able to represent the skin color properly, it is needed to be processed in the appropriate color space. This study examines some color spaces to determine the most appropriate color space in detecting skin color. The color spaces in this case are RGB, HSV, HSL, YIQ, YUV, YCbCr, YPbPr, YDbDr, CIE XYZ, CIE L*a*b*, CIE L*u* v*, and CIE L*ch. Based on the test results using 400 image data consisting of 200 skin images and 200 non-skin images, it is obtained that the most appropriate color space to detect the color is CIE L*u*v*.

A Novel Piece-Wise Linear Algorithm for Human Skin Segmentation

2015 ◽  
Vol 743 ◽  
pp. 317-320
Author(s):  
Ravi Subban ◽  
Pasupathi Perumalsamy ◽  
G. Annalakshmi
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Skin Detection ◽  
Color Spaces ◽  
Color Component ◽  
Skin Segmentation ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Comprehensive Knowledge ◽  
Novel Method

This paper presents a novel method for skin segmentation in color images using piece-wise linear bound skin detection. Various color schemes are investigated and evaluated to find the effect of color space transformation over the skin detection performance. The comprehensive knowledge about the various color spaces helps in skin color modeling evaluation. The absence of the luminance component increases performance, which also supports in finding the appropriate color space for skin detection. The single color component produces the better performance than combined color component and reduces computational complexity.

Pencarian File Gambar Berdasarkan Dominasi Warna

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Vina Chovan Epifania ◽  
Eko Sediyono
Keyword(s):  
Digital Image ◽  
Retrieval System ◽  
Color Image ◽  
Color Space ◽  
Image Data ◽  
Image Search ◽  
Test Results ◽  
Application Form ◽  
Space Model ◽  
Image File

Abstract. Image File Searching Based on Color Domination. One characteristic of an image that can be used in image searching process is the composition of the colors. Color is a trait that is easily seen by man in the picture. The use of color as a searching parameter can provide a solution in an easier searching for images stored in computer memory. Color images have RGB values that can be computed and converted into HSL color space model. Use of HSL images model is very easy because it can be calculated using a percent, so that in each pixel of the image can be grouped and named, this can give a dominant values of the colors contained in one image. By obtaining these values, the image search can be done quickly just by using these values to a retrieval system image file. This article discusses the use of the HSL color space model to facilitate the searching for a digital image in the digital image data warehouse. From the test results of the application form, a searching is faster by using the colors specified by the user. Obstacles encountered were still searching with a choice of 15 basic colors available, with a limit of 33% dominance of the color image search was not found. This is due to the dominant color in each image has the most dominant value below 33%.   Keywords: RGB, HSL, image searching Abstrak. Salah satu ciri gambar yang dapat dipergunakan dalam proses pencarian gambar adalah komposisi warna. Warna adalah ciri yang mudah dilihat oleh manusia dalam citra gambar. Penggunaan warna sebagai parameter pencarian dapat memberikan solusi dalam memudahkan pencarian gambar yang tersimpan dalam memori komputer. Warna gambar memiliki nilai RGB yang dapat dihitung dan dikonversi ke dalam model HSL color space. Penggunaan model gambar HSL sangat mudah karena dapat dihitung dengan menggunakan persen, sehingga dalam setiap piksel gambar dapat dikelompokan dan diberi nama, hal ini dapat memberikan suatu nilai dominan dari warna yang terdapat dalam satu gambar. Dengan diperolehnya nilai tersebut, pencarian gambar dapat dilakukan dengan cepat hanya dengan menggunakan nilai tersebut pada sistem pencarian file gambar. Artikel ini membahas tentang penggunaan model HSL color space untuk mempermudah pencarian suatu gambar digital didalam gudang data gambar digital. Dari hasil uji aplikasi yang sudah dibuat, diperoleh pencarian yang lebih cepat dengan menggunakan pilihan warna yang ditentukan sendiri oleh pengguna. Kendala yang masih dijumpai adalah pencarian dengan pilihan 15 warna dasar yang tersedia, dengan batas dominasi warna 33% tidak ditemukan gambar yang dicari. Hal ini disebabkan warna dominan disetiap gambar kebanyakan memiliki nilai dominan di bawah 33%. Kata Kunci: RGB, HSL, pencarian gambar

Human Body Parts Detection Using YCbCr Color Space

2013 ◽  
Vol 393 ◽  
pp. 556-560
Author(s):  
Nurul Fatiha Johan ◽  
Yasir Mohd Mustafah ◽  
Nahrul Khair Alang Md Rashid
Keyword(s):  
Human Body ◽  
Skin Color ◽  
Color Space ◽  
Detection Algorithm ◽  
Color Segmentation ◽  
Color Model ◽  
Body Parts ◽  
Color Spaces ◽  
Morphological Operation ◽  
Ycbcr Color Space

Skin color is proved to be very useful technique for human body parts detection. The detection of human body parts using skin color has gained so much attention by many researchers in various applications especially in person tracking, search and rescue. In this paper, we propose a method for detecting human body parts using YCbCr color spaces in color images. The image captured in RGB format will be transformed into YCbCr color space. This color model will be converted to binary image by using color thresholding which contains the candidate human body parts like face and hands. The detection algorithm uses skin color segmentation and morphological operation.

An Eye Localization Method Based on the Skin Color Feature and Otsu Algorithm

2013 ◽  
Vol 401-403 ◽  
pp. 1324-1327 ◽  
Author(s):  
Xiu Fang Yang ◽  
Yin Cheng Qi ◽  
Jie Yao
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Color Segmentation ◽  
Search Region ◽  
Color Feature ◽  
Integral Projection ◽  
Eye Localization ◽  
Segmented Image ◽  
Facial Area ◽  
Left And Right

In order to eliminate the influences of illumination and face-poses on eye localization, a feasible method is proposed based on the skin color feature and Otsu algorithm. Firstly, we detect the skin color in YCb'Cr' color space. Skin color segmentation principle is used to narrow the search region in human eye detection. Then we convert the segmented image to a binary image by Otsu algorithm and extract the eye region. Finally, the left and right eyes are positioned in the facial area with the binary integral projection. An analysis of the detections reveals that this algorithm has good robustness against changes of illumination and face-pose.

scholarly journals A multi-color based features from facial images for automatic ethnicity identification model

2020 ◽  
Vol 18 (3) ◽  
pp. 1383
Author(s):  
Mohd Zamri Osman ◽  
Mohd Aizaini Maarof ◽  
Mohd Foad Rohani ◽  
Nilam Nur Amir Sjarif ◽  
Nor Saradatul Akmar Zulkifli
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Skin Color ◽  
Color Space ◽  
Experimental Result ◽  
Demographic Information ◽  
Feature Selection Technique ◽  
Color Feature ◽  
Biometric Analysis ◽  
Identification Model

<span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification technique using color-based feature mostly failed to determine the ethnicity classes accurately due to low properties of features in color-based. Thus, this paper purposely analyses the accuracy of the color-based ethnicity identification model from various color spaces. The proposed model involved several phases such as skin color feature extraction, feature selection, and classification. In the feature extraction process, a dynamic skin color detection is adapted to extract the skin color information from the face candidate. The multi-color feature was formed from the descriptive statistical model. Feature selection technique applied to reduce the feature space dimensionality. Finally, the proposed ethnicity identification was tested using several classification algorithms. From the experimental result, we achieved a better result in multi-color feature compared to individual color space model under Random Forest algorithm.</span>

Human Skin Color Detection Using Neural Networks

2015 ◽  
Vol 24 (4) ◽  
pp. 425-436 ◽  
Author(s):  
Mohammadreza Hajiarbabi ◽  
Arvin Agah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Human Skin ◽  
Skin Color ◽  
Skin Detection ◽  
Color Spaces ◽  
Skin Color Detection ◽  
The Neural Network ◽  
Color Detection ◽  
Different Color

AbstractHuman skin detection is an essential phase in face detection and face recognition when using color images. Skin detection is very challenging because of the differences in illumination, differences in photos taken using an assortment of cameras with their own characteristics, range of skin colors due to different ethnicities, and other variations. Numerous methods have been used for human skin color detection, including the Gaussian model, rule-based methods, and artificial neural networks. In this article, we introduce a novel technique of using the neural network to enhance the capabilities of skin detection. Several different entities were used as inputs of a neural network, and the pros and cons of different color spaces are discussed. Also, a vector was used as the input to the neural network that contains information from three different color spaces. The comparison of the proposed technique with existing methods in this domain illustrates the effectiveness and accuracy of the proposed approach. Tests were done on two databases, and the results show that the neural network has better precision and accuracy rate, as well as comparable recall and specificity, compared with other methods.

scholarly journals An improved human skin detection and localization by using machine learning techniques in RGB and YCbCr color spaces

2019 ◽  
Author(s):  
Mohammad Mortazavi T. ◽  
Omid Mahdi Ebadati E.
Keyword(s):  
Human Skin ◽  
Skin Color ◽  
Wide Spectrum ◽  
Region Of Interest ◽  
Human Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Skin Detection ◽  
Color Spaces ◽  
Detection And Localization

Human Skin Detection is one of the most applicable methods in human detection, face detection and so many other detections. These processes can be used in a wide spectrum like industry, medicine, security, etc. The objective of this work is to provide an accurate and efficient method to detect human skin in images. This method can detect and classify skin pixels and reduce the size of images. With the use of RGB and YCbCr color spaces, proposed approach can localize a Region Of Interest (ROI) that contains skin pixels. This method consists of three steps. In the first stage, pre-processing an image like normalization, detecting skin range from the dataset, etc. is done. In the second stage, the proposed method detects candidate’s pixels that are in the range of skin color. In the third stage, with the use of a classifier, it decreases unwanted pixels and areas to decrease the accuracy of the region. The results show 97% sensitivity and 85% specificity for support vector machine classifier.

scholarly journals Image Segmentation for Skin Detection

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Sajaa G. Mohammed ◽  
Abdulrahman H. Majeed ◽  
Ali Aldujaili ◽  
Enas Kh. Hassan ◽  
Safa S. Abdul-Jabbar
Keyword(s):  
Image Segmentation ◽  
Skin Color ◽  
Detection System ◽  
Color Space ◽  
Research Direction ◽  
Parametric Models ◽  
Future Research ◽  
Skin Detection ◽  
Ycbcr Color Space ◽  
Human Faces

Human skin detection, which usually performed before image processing, is the method of discovering skin-colored pixels and regions that may be of human faces or limbs in videos or photos. Many computer vision approaches have been developed for skin detection. A skin detector usually transforms a given pixel into a suitable color space and then uses a skin classifier to mark the pixel as a skin or a non-skin pixel. A skin classifier explains the decision boundary of the class of a skin color in the color space based on skin-colored pixels. The purpose of this research is to build a skin detection system that will distinguish between skin and non-skin pixels in colored still pictures. This performed by introducing a metric that measures the distances of pixel colors to skin tones. Results showed that the YCbCr color space performed better skin pixel detection than regular Red Green Blue images due to its isolation of the overall energy of an image in the luminance band. The RGB color space poorly classified images with wooden backgrounds or objects. Then, a histogram-based image segmentation scheme utilized to distinguish between the skin and non-skin pixels. The need for a compact skin model representation should stimulate the development of parametric models of skin detection, which is a future research direction.

Application of Skin Detection Based on Irregular Polygon Area Boundary Constraint on YCbCr and Reverse Gamma Correction

2011 ◽  
Vol 327 ◽  
pp. 31-36
Author(s):  
Bao Song Wang ◽  
Xue Qiang Lv ◽  
Xin Long Ma ◽  
Hong Wei Wang
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Real Life ◽  
Skin Detection ◽  
Gamma Correction ◽  
Ycbcr Color Space ◽  
Boundary Constraint ◽  
Polygon Area ◽  
Space Experiments ◽  
Better Than

YCbCr color space is widely used in skin detection. An improved method is brought up in this paper: a method based on irregular polygon area boundary constraint on YCbCr color space. Experiments shows that this method get more accurate distribution of skin color in YCbCr color space and lower the false detection rate while keeps the precision rate. In consideration of that the value of pix on image is not the real pix in real life, an improved reverse Gamma correction is brought up for solve a problem in reverse Gamma correction. Experiment result shows that improved reverse Gamma correction is better than none improved reverse Gamma correction.

A Hybrid Skin Detection Model from Multiple Color Spaces Based on a Dual-Threshold Bayesian Algorithm

2016 ◽  
Vol 30 (07) ◽  
pp. 1655018 ◽  
Author(s):  
Fujunku Chen ◽  
Zhigang Hu ◽  
Keqin Li ◽  
Wei Liu
Keyword(s):  
False Positive Rate ◽  
Recognition Rate ◽  
Color Space ◽  
True Positive Rate ◽  
Skin Detection ◽  
Color Spaces ◽  
Bayesian Algorithm ◽  
Detection Model ◽  
Positive Rate ◽  
Specific Rule

As a preliminary step of many applications, skin detection serves as an irreplaceable role in image processing applications, such as face recognition, gesture recognition, web image filtering, and image retrieval systems. Combining information from multiple color spaces improves the recognition rate and reduces the error rate because the same color is represented differently in other color spaces. Consequently, a hybrid skin detection model from multiple color spaces based on a dual-threshold Bayesian algorithm (DTBA) has been proposed. In each color space, the pixels of images are divided into three categories, namely, skin, nonskin, and undetermined, when using the DTBA. Then, nearly all skin pixels are obtained by using a specific rule that combines the recognition results from multiple color spaces. Furthermore, skin texture filtering and morphological filtering are applied to the results by effectively reducing false identified pixels. In addition, the proposed skin model can overcome interference from a complex background. The method has been validated in a series of experiments using the Compaq and the high-resolution image datasets (HRIDs). The findings have demonstrated the proposed approach produced an improvement, the true positive rate (TPR) improves more than 6% and the false positive rate (FPR) reduces more than 11%, compared with the Bayesian classifier. We confirm that the method is competitive. Meanwhile, this model is robust against skin distribution, scaling, partial occlusions, and illumination variations.

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