Face detection in complex background based on Adaboost algorithm and YCbCr skin color model

2015 ◽  
Author(s):  
Wei Ge ◽  
Chunling Han ◽  
Wei Quan
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Model ◽  
Adaboost Algorithm ◽  
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.

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