scholarly journals A Study on the YCbCr Color Model and the Rough Set for a Robust Face Detection Algorithm

2011 ◽  
Vol 16 (7) ◽  
pp. 117-125
Author(s):  
Oh-Sung Byun
Keyword(s):  
Face Detection ◽  
Rough Set ◽  
Detection Algorithm ◽  
Color Model

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.

Face Detection Based on Statistical Color Model and Haar Classifier

2012 ◽  
Vol 532-533 ◽  
pp. 634-638
Author(s):  
Xi Bin Jia ◽  
Lu Yi Li
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Speech Synthesis ◽  
Detection Algorithm ◽  
Visual Speech ◽  
Training Dataset ◽  
Color Model ◽  
Skin Area ◽  
Skin Model ◽  
The Face

The paper realizes the face detection algorithm based on the combination of the skin model and the Haar algorithm. Firstly, a platform for sample labeling was constructed, which combines the contour extraction algorithm with manual labeling. By labeling more than 10000 images obtained randomly from the Internet, a large training dataset is available. Then, a skin histogram, a non-skin histogram and a statistical skin model are constructed by analyzing the distribution of the skin and the non-skin color on the basis of a large training dataset. Based on this statistical color model, the skin area is detected and split from video files frame by frame. With the Haar Object Detection algorithm and the morphology algorithm such as erosion and dilation, the background noise and non-face areas are removed from the detected skin area and facial area is detected, which provides the basis for face recognition and the video-based visual speech synthesis. Compared with the Haar-based face detection method, our algorithm greatly improves the rate of correct detection and reduces the rate of the false positives.

Fast Face Detection Algorithm Based on Improved Skin-Color Model

2012 ◽  
Vol 38 (3) ◽  
pp. 629-635 ◽  
Author(s):  
Zhen-Xue Chen ◽  
Cheng-Yun Liu ◽  
Fa-Liang Chang ◽  
Xu-Zhen Han
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Algorithm ◽  
Color Model ◽  
Skin Color Model

Moving Objects Detection Algorithm Based on Three-Dimensional Gaussian Mixture Codebook Model Using XYZ Color Model

2013 ◽  
Vol 347-350 ◽  
pp. 3505-3509 ◽  
Author(s):  
Jin Huang ◽  
Wei Dong Jin ◽  
Na Qin
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Three Dimensional ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Color Model ◽  
Moving Objects Detection ◽  
Objects Detection ◽  
Model Algorithm

In order to reduce the difficulty of adjusting parameters for the codebook model and the computational complexity of probability distribution for the Gaussian mixture model in intelligent visual surveillance, a moving objects detection algorithm based on three-dimensional Gaussian mixture codebook model using XYZ color model is proposed. In this algorithm, a codebook model based on XYZ color model is built, and then the Gaussian model based on X, Y and Z components in codewords is established respectively. In this way, the characteristic of the three-dimensional Gaussian mixture model for the codebook model is obtained. The experimental results show that the proposed algorithm can attain higher real-time capability and its average frame rate is about 16.7 frames per second, while it is about 8.3 frames per second for the iGMM (improved Gaussian mixture model) algorithm, about 6.1 frames per second for the BM (Bayes model) algorithm, about 12.5 frames per second for the GCBM (Gaussian-based codebook model) algorithm, and about 8.5 frames per second for the CBM (codebook model) algorithm in the comparative experiments. Furthermore the proposed algorithm can obtain better detection quantity.

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