Paper Info
Title
A Face Detection Method Based On Skin Color Model And Improved Adaboost Algorithm
Abstract
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 swami 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.
Year
DOI
Venue
2020
10.18280/ts.370606
TRAITEMENT DU SIGNAL
Keywords
DocType
Volume
face detection, image processing, skin color model, AdaBoost algorithm
Journal
37
Issue
ISSN
Citations 
6
0765-0019
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xiaoying Yang152.22
Nannan Liang200.34
Wei Zhou300.34
Hongmei Lu400.34