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 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.

Illumination-adaptive skin color detection method

2011 ◽  
Vol 31 (5) ◽  
pp. 1233-1236
Author(s):  
Xia XIONG ◽  
Qing-bing SANG
Keyword(s):  
Skin Color ◽  
Detection Method ◽  
Skin Color Detection ◽  
Color Detection

Skin Color Detection Method Based on Direct Least Square Ellipse Fitting in CrCbCg Space

2011 ◽  
Vol 366 ◽  
pp. 28-31 ◽  
Author(s):  
Jin Guang Sun ◽  
Yu Cheng Zhou
Keyword(s):  
Skin Color ◽  
Detection Method ◽  
Three Dimensional ◽  
Least Square ◽  
Ellipse Fitting ◽  
Complex Environment ◽  
Color Distribution ◽  
Skin Color Detection ◽  
Color Detection ◽  
Fitting In

Skin color detection is an important in computer vision.This work presents a new efficient method for skin color detection based on an improved direct least square ellipse fitting in the space CrCbCg. The color distribution statistics of three-dimensional CrCbCg is obtained by fitting the color distribution ellipse boundary in the Cr-Cb, Cr-Cg, Cb-Cg plane exactly ,finally ,the criteria based on the statistics is used to detect skin color area accurately. Experimental results show that this method improves the robustness of the skin color detection to complex environment.

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.

Sign in / Sign up

Export Citation Format

Share Document