A New Face Detection Method Based on Improved Adaboost and Skin Color Model in Complex Background

2014 ◽  
Vol 11 (18) ◽  
pp. 6753-6762
Author(s):  
Yang Xia
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Color Model ◽  
Complex Background ◽  
Skin Color Model

scholarly journals A Face Detection Method Based on Skin Color Model and Improved AdaBoost Algorithm

2020 ◽  
Vol 37 (6) ◽  
pp. 929-937
Author(s):  
Xiaoying Yang ◽  
Nannan Liang ◽  
Wei Zhou ◽  
Hongmei Lu
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Rate ◽  
Detection Method ◽  
Random Search ◽  
Color Space ◽  
Color Model ◽  
High Detection Rate ◽  
Adaboost Algorithm ◽  
Skin Color Model

This paper integrates skin color model and improved AdaBoost into a face detection method for high-resolution images with complex backgrounds. Firstly, the skin color areas were detected in a multi-color space. Each image was subject to adaptive brightness compensation, and converted into the YCbCr space, and a skin color model was established to solve face similarity. After eliminating the background interference by morphological method, the skin color areas were segmented to obtain the candidate face areas. Next, the inertia weight control factors and random search factor were introduced to optimize the global search ability of particle swarm optimization (PSO). The improved PSO was adopted to optimize the initial connection weights and output thresholds of the neural network. After that, a strong AdaBoost classifier was designed based on optimized weak BPNN classifiers, and the weight distribution strategy of AdaBoost was further improved. Finally, the improved AdaBoost was employed to detect the final face areas among the candidate areas. Simulation results show that our face detection method achieved high detection rate at a fast speed, and lowered false detection rate and missed detection rate.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document