scholarly journals PKT: fast color-based spatial model for human skin detection

2021 ◽  
Author(s):  
Grace L. Samson ◽  
Joan Lu
Keyword(s):  
Spatial Structure ◽  
Human Skin ◽  
Skin Color ◽  
Skin Detection ◽  
Quad Tree ◽  
Learning Tasks ◽  
Color Detection ◽  
Speed Up ◽  
Image Pixels ◽  
Speed Accuracy

AbstractWe present a new detection method for color-based object detection, which can improve the performance of learning procedures in terms of speed, accuracy, and efficiency, using spatial inference, and algorithm. We applied the model to human skin detection from an image; however, the method can also work for other machine learning tasks involving image pixels. We propose (1) an improved RGB/HSL human skin color threshold to tackle darker human skin color detection problem. (2), we also present a new rule-based fast algorithm (packed k-dimensional tree --- PKT) that depends on an improved spatial structure for human skin/face detection from colored 2D images. We also implemented a novel packed quad-tree (PQT) to speed up the quad-tree performance in terms of indexing. We compared the proposed system to traditional pixel-by-pixel (PBP)/pixel-wise (PW) operation, and quadtree based procedures. The results show that our proposed spatial structure performs better (with a very low false hit rate, very high precision, and accuracy rate) than most state-of-the-art models.

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.

Human Skin Detection in Color Images Using Deep Learning

2015 ◽  
Vol 5 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Mohammadreza Hajiarbabi ◽  
Arvin Agah
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Human Skin ◽  
Skin Color ◽  
Color Image ◽  
Gaussian Model ◽  
Color Images ◽  
Skin Detection ◽  
Learning Methods ◽  
Rule Based

Human skin detection is an important and challenging problem in computer vision. Skin detection can be used as the first phase in face detection when using color images. The differences in illumination and ranges of skin colors have made skin detection a challenging task. Gaussian model, rule based methods, and artificial neural networks are methods that have been used for human skin color detection. Deep learning methods are new techniques in learning that have shown improved classification power compared to neural networks. In this paper the authors use deep learning methods in order to enhance the capabilities of skin detection algorithms. Several experiments have been performed using auto encoders and different color spaces. The proposed technique is evaluated compare with other available methods in this domain using two color image databases. The results show that skin detection utilizing deep learning has better results compared to other methods such as rule-based, Gaussian model and feed forward neural network.

SHAPE-BASED ADULT IMAGE DETECTION

2006 ◽  
Vol 06 (01) ◽  
pp. 115-124 ◽  
Author(s):  
QING-FANG ZHENG ◽  
WEI ZENG ◽  
WEI-QIANG WANG ◽  
WEN GAO
Keyword(s):  
Skin Color ◽  
Detection Method ◽  
Skin Detection ◽  
Shape Descriptors ◽  
Shape Features ◽  
Image Detection ◽  
Classification Schemes ◽  
Skin Color Detection ◽  
Color Detection ◽  
Boosted Classifiers

This paper investigates adult images detection based on the shape features of skin regions. In order to accurately detect skin regions, we propose a skin detection method using multi-Bayes classifiers in the paper. Based on skin color detection results, shape features are extracted and fed into a boosted classifier to decide whether or not the skin regions represent a nude. We evaluate adult image detection performance using different boosted classifiers and different shape descriptors. Experimental results show that classification using boosted C4.5 classifier and combination of different shape descriptors outperforms other classification schemes.

NEURAL NETWORK APPROACH FOR IMAGE CHROMATIC ADAPTATION FOR SKIN COLOR DETECTION

2007 ◽  
Vol 17 (01) ◽  
pp. 1-12 ◽  
Author(s):  
N. BOURBAKIS ◽  
P. KAKUMANU ◽  
S. MAKROGIANNIS ◽  
R. BRYLL ◽  
S. PANCHANATHAN
Keyword(s):  
Neural Networks ◽  
Human Skin ◽  
Skin Color ◽  
Chromatic Adaptation ◽  
White Patch ◽  
Neural Network Approach ◽  
Data Set ◽  
New Approach ◽  
Color Detection ◽  
Color Data

The goal of image chromatic adaptation is to remove the effect of illumination and to obtain color data that reflects precisely the physical contents of the scene. We present in this paper an approach to image chromatic adaptation using Neural Networks (NN) with application for detecting — adapting human skin color. The NN is trained on randomly chosen color images containing human subject under various illuminating conditions, thereby enabling the model to dynamically adapt to the changing illumination conditions. The proposed network predicts directly the illuminant estimate in the image so as to adapt to human skin color. The comparison of our method with Gray World, White Patch and NN on White Patch methods for skin color stabilization is presented. The skin regions in the NN stabilized images are successfully detected using a computationally inexpensive thresholding operation. We also present results on detecting skin regions on a data set of test images. The results are promising and suggest a new approach for adapting human skin color using neural networks.

Human Skin Color Detection Technique Using Different Color Models

2021 ◽  
pp. 261-279
Author(s):  
Ruqaiya Khanam ◽  
Prashant Johri ◽  
Mario José Diván
Keyword(s):  
Human Skin ◽  
Skin Color ◽  
Detection Technique ◽  
Color Models ◽  
Skin Color Detection ◽  
Color Detection ◽  
Different Color

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.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

2008 ◽  
Vol 22 (06) ◽  
pp. 1241-1265
Author(s):  
WALDEMAR BONVENTI ◽  
ANNA HELENA REALI COSTA
Keyword(s):  
Human Skin ◽  
Fuzzy Clustering ◽  
Skin Color ◽  
Aggregation Operators ◽  
Skin Detection ◽  
Training Phase ◽  
Skin Model ◽  
Class Model ◽  
Training Images ◽  
Fuzzy Learning

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised- and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

scholarly journals Filter Halaman Web Pornografi Menggunakan Kecocokan Kata dan Deteksi Warna Kulit

CAUCHY ◽  
2011 ◽  
Vol 1 (4) ◽  
pp. 207 ◽  
Author(s):  
Yusron Rijal ◽  
Awalia Nofitasari
Keyword(s):  
Image Processing ◽  
Human Skin ◽  
Skin Color ◽  
Traditional Method ◽  
Positive Relationship ◽  
Color Model ◽  
New Approach ◽  
Skin Color Detection ◽  
Color Detection ◽  
Text Filtering

This paper presents an effort to detect pornographic webpages. It was stated that a positive relationship exists between percentage of human skin color in an image and the image itself (Jones et.al., 1998). Based on the statement, rather than using the traditional method of text-filtering, this paper propose a new approach to detect pornographic images by using skin color detection. The skin color detection performed by using RGB, HSI, and YCbCr color model. Using algorithm stated by Ap-apid (Ap-apid, 2005), the system will classify nude and not-nude images. If one or more nude images are found, the system will block the webpage. Keywords: Webpage Filtering, Image Processing, Pornography, Nudity, Skin Color, Nude Images

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