Face detection in color images using skin color model algorithm based on skin color information

2011 ◽  
Author(s):  
D N Chandrappa ◽  
M Ravishankar ◽  
D R RameshBabu
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Images ◽  
Color Model ◽  
Color Information ◽  
Skin Color Model ◽  
Model Algorithm

Face Localization in Color Images Based on Skin Color and Eye Gradient

2011 ◽  
Vol 268-270 ◽  
pp. 1382-1385
Author(s):  
Yun Juan Liang ◽  
Xiao Ying Wu ◽  
Li Juan Ma ◽  
Li Jun Zhang
Keyword(s):  
Skin Color ◽  
Color Images ◽  
Color Model ◽  
Human Face ◽  
Color Information ◽  
Eye Localization ◽  
Illumination Changes ◽  
Skin Color Model ◽  
Candidate Regions ◽  
Face Localization

In color images, skin color is the important information on human face. This paper proposes a method to detect and locate human face rapidly based on skin color information and eye gradient. First, normalized RGB space is converted to HSV space; Secondly, the images are pretreated by smoothing and light compensation to overcome the uneven illumination changes, and then the defined skin color model is used to determine candidate regions of the human face, finally the human face is located accurately through eye localization based on gradient template. Experiments show that the method is fast and effective.

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.

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