An Approach to Threshold Based Human Skin Color Recognition and Segmentation in Different Color Models

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.

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Techniques for Skin, Face, Eye and Lip Detection using Skin Segmentation in Color Images

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2015070103 ◽

2015 ◽

Vol 5 (2) ◽

pp. 35-57 ◽

Cited By ~ 2

Author(s):

Mohammadreza Hajiarbabi ◽

Arvin Agah

Keyword(s):

Neural Networks ◽

Human Skin ◽

Face Detection ◽

Skin Color ◽

Color Images ◽

Detection Methods ◽

Skin Segmentation ◽

The Face ◽

Different Color ◽

Lip Detection

Face detection is a challenging and important problem in Computer Vision. In most of the face recognition systems, face detection is used in order to locate the faces in the images. There are different methods for detecting faces in images. One of these methods is to try to find faces in the part of the image that contains human skin. This can be done by using the information of human skin color. Skin detection can be challenging due to factors such as the differences in illumination, different cameras, ranges of skin colors due to different ethnicities, and other variations. Neural networks have been used for detecting human skin. Different methods have been applied to neural networks in order to increase the detection rate of the human skin. The resulting image is then used in the detection phase. The resulting image consists of several components and in the face detection phase, the faces are found by just searching those components. If the components consist of just faces, then the faces can be detected using correlation. Eye and lip detections have also been investigated using different methods, using information from different color spaces. The speed of face detection methods using color images is compared with other face detection methods.

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

Multimedia Tools and Applications ◽

10.1007/s11042-021-10955-4 ◽

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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Face Image Segmentation Using Color Information and Saliency Map

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.55-57.77 ◽

2011 ◽

Vol 55-57 ◽

pp. 77-81

Author(s):

Hui Ming Huang ◽

He Sheng Liu ◽

Guo Ping Liu

Keyword(s):

Image Segmentation ◽

Human Skin ◽

Skin Color ◽

Saliency Map ◽

Face Image ◽

Color Information ◽

Attention Model ◽

Face Area ◽

Face Region ◽

The Face

In this paper, we proposed an efficient method to address the problem of color face image segmentation that is based on color information and saliency map. This method consists of three stages. At first, skin colored regions is detected using a Bayesian model of the human skin color. Then, we get a chroma chart that shows likelihoods of skin colors. This chroma chart is further segmented into skin region that satisfy the homogeneity property of the human skin. The third stage, visual attention model are employed to localize the face region according to the saliency map while the bottom-up approach utilizes both the intensity and color features maps from the test image. Experimental evaluation on test shows that the proposed method is capable of segmenting the face area quite effectively，at the same time, our methods shows good performance for subjects in both simple and complex backgrounds, as well as varying illumination conditions and skin color variances.

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Behavior of skin color under varying illumination seen by different cameras at different color spaces

10.1117/12.420902 ◽

2001 ◽

Cited By ~ 19

Author(s):

J. Birgitta Martinkauppi ◽

Maricor N. Soriano ◽

Mika V. Laaksonen

Keyword(s):

Skin Color ◽

Color Spaces ◽

Different Color

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Human Skin Detection in Color Images Using Deep Learning

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2015070101 ◽

2015 ◽

Vol 5 (2) ◽

pp. 1-13 ◽

Cited By ~ 1

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.

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