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.

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

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 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

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.

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