Face Image Segmentation Technology Research

2013 ◽  
Vol 846-847 ◽  
pp. 1339-1342
Author(s):  
Chang Jie Hu ◽  
Hong Li Xu
Keyword(s):  
Skin Color ◽  
Color Image ◽  
Face Image ◽  
Personal Identification ◽  
Dynamic Threshold ◽  
Color Model ◽  
Gray Image ◽  
Other Information ◽  
The Face ◽  
Skin Color Model

Face contains the very rich information, which is a typical biological feature .It has a wide application prospect in personal identification, intelligent video surveillance and human-computer interaction. Face detection is to determine the number, the location, size and other information of all the faces among the color images that have been input. Firstly, skin color model is established, and then we use the skin color model to convert color image to gray image, and then we can denoise gray image, at last use the Fisher criterion to obtain the dynamic threshold segmentation of the face image, so as to lay a good foundation for the location of the face region. Through the experiment we can see, the selection of dynamic threshold, for different detecting images, obtained better color segmentation.

Research and Improvement on the Algorithm of Face Region Detection Based on Skin Color Model

2014 ◽  
Vol 543-547 ◽  
pp. 2702-2705
Author(s):  
Hong Hai Liu ◽  
Xiang Hua Hou
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Algorithm ◽  
Face Image ◽  
Color Model ◽  
Region Detection ◽  
Face Region ◽  
The Face ◽  
Skin Color Model ◽  
Better Than

In face image with complex background, the CbCr skin color region will have offset when considering the illumination change. Therefore, the non-skin color pixels which luminance is less than 80 will be mistaken as skin color pixels and the skin color pixels which luminance is greater than 230 will be mistaken as non-skin color pixels. In order to reduce the misjudgments, an improved skin color model of nonlinear piecewise is put forward in this paper. Firstly, the skin color model of non-piecewise is analyzed and the experimental results show that by this model there is an obvious misjudgment in face detection. Then the skin color model of nonlinear piecewise is mainly analyzed and is demonstrated by mathematics method. A large number of training results show that the skin color model of nonlinear piecewise has better clustering distribution than the skin color model of non-piecewise. At lastly, the face detection algorithm adopting skin color model of nonlinear piecewise is analyzed. The results show that this algorithm is better than the algorithm adopting skin color model of non-piecewise and it makes a good foundation for the application of face image.

The System of Face Recognition Design

2014 ◽  
Vol 644-650 ◽  
pp. 3943-3946
Author(s):  
Xiao Bin Yu ◽  
Zi Qiao Li ◽  
Wen Qiang Ke ◽  
Rui Peng Li ◽  
Kai Xiong
Keyword(s):  
Face Recognition ◽  
Skin Color ◽  
Color Space ◽  
Color Model ◽  
Face Images ◽  
Accurate Detection ◽  
Haar Features ◽  
The Face ◽  
The Media ◽  
Skin Color Model

The technology of face recognition is the media to face images as the identity of the face recognition system.Through the choice of color space and the establishment of skin color model, give a rough detection for the human's image, then use the face Haar features getting more accurate detection.

Human Face Detection Method Based on Skin Color Model

2013 ◽  
Vol 706-708 ◽  
pp. 1877-1881
Author(s):  
San Tang
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Color Space ◽  
Color Model ◽  
Knowledge Based ◽  
Face Area ◽  
The Face ◽  
Skin Color Model ◽  
Active Research

Face detection is the first step of face recognition, and is a very active research topic in the filed of computer vision and pattern recognition. A skin color model based face detection method for chromatic images is proposed in this paper. The H-CgCr skin color model is established by taking advantage of the color pixels clustering distribution in the HSV and YCgCr color space. The noises are eliminated based on skin color segmentation, and the face candidate region is judged according to knowledge-based, finally, the position of the face area is marked by the box. The experimental results demonstrate that the proposed method is feasible and effective.

scholarly journals ANALISIS IDENTIFIKASI WAJAH MENGGUNAKAN GABOR FILTER DAN SKIN MODEL

2019 ◽  
Vol 2 (2) ◽  
pp. 150
Author(s):  
Khairunnisa Khairunnisa ◽  
Rismayanti Rismayanti ◽  
Rully Alhari
Keyword(s):  
Skin Color ◽  
Digital Images ◽  
Gabor Filter ◽  
Gabor Filters ◽  
Color Model ◽  
Face Area ◽  
The Face ◽  
Skin Color Model ◽  
Facial Identification ◽  
Facial Images

Abstract - Identification of faces in digital images is a complex process and requires a combination of various methods. The complexity of facial identification is increasing along with the increasing need for high accuracy of facial images. This research analyzes the combination of Skin Color Model and Gabor Filters in the process of identifying facial identities in digital images. The Skin Color Model method is used to separate the face area from facial images based on skin color values on facial images. The face area is then extracted using Gabor Filter. This research resulted in the highest accuracy was 93.6349%. and the lowest accuracy is around 82.45%. The implementation of a combination of Skin Color Models and Gabor Filters can be an alternative method of identifying faces in digital images. Keywords - Digital Image, Face Identification, Skin Color Model, Gabor Filter. 

Face Detection Method Using PCNN and Skin Color Model

2012 ◽  
Vol 562-564 ◽  
pp. 1377-1381
Author(s):  
Dong Ming Zhou ◽  
Hong Cai
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Color Image ◽  
Ignition Time ◽  
Color Space ◽  
Target Face ◽  
Color Model ◽  
Skin Area ◽  
Skin Color Model

This paper presented a face detection method for the color image using pulse coupled neural network (PCNN) and skin color model. The color image which is processed well through light compensation is converted from RGB to YCbCr color space, then the skin area are divided into sub-block, and skin color segmentation is made for the image in YCbCr space. Finally, we use PCNN to extract all sub-block ignition time sequence, and calculate various sub-block difference degrees between target face and the tested image, if the difference degree is the smallest, then the target face himself is the same person. Experimental results show that the proposed method has higher accuracy and robustness, can obtain satisfactory detection effect.

Face Image Segmentation Using Color Information and Saliency Map

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.

An Improved Adaptive Threshold Skin Color Model

2014 ◽  
Vol 610 ◽  
pp. 358-361
Author(s):  
Hong Wei Di ◽  
Wei Xu
Keyword(s):  
Skin Color ◽  
Adaptive Threshold ◽  
Experimental Results ◽  
Color Model ◽  
Threshold Segmentation ◽  
Change Rate ◽  
Skin Segmentation ◽  
Strong Robustness ◽  
Skin Color Model ◽  
Optimum Threshold

To solve the problem that traditional threshold segmentation model is not very robust in skin segmentation under different skin colors and different illuminations, an improved adaptive skin color model is proposed. This model detects the change rate of the skin color pixels by modifying the certain threshold while fixing others, then selects the optimum threshold adaptively. The experimental results show that this algorithm can effectively distinguish skin color regions and background regions, and has strong robustness on light disturbance.

Sign in / Sign up

Export Citation Format

Share Document