The research of face detection method based on adaboost algorithm and skin color segmentation

2017 ◽  
Author(s):  
Liu Chang
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Color Segmentation ◽  
Adaboost Algorithm

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.

Face Detection Based on Skin Color and AdaBoost Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1590-1594
Author(s):  
Yu Wang ◽  
Xiao Juan Ban ◽  
Xing Yang ◽  
Yi Fei Guo
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Experimental Results ◽  
Detection Methods ◽  
Color Segmentation ◽  
False Alarms ◽  
Color Feature ◽  
Adaboost Algorithm

Skin color segmentation and AdaBoost algorithm always play important roles in various face detection methods. To combine the two smoothly, this paper investigates face detection methods based on skin color feature and AdaBoost algorithm. Experimental results show that the proposed methods can effectively reduce the false alarms.

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

2021 ◽  
Vol 1748 ◽  
pp. 042015
Author(s):  
He Yan ◽  
Yuhan Liu ◽  
Xiaotang Wang ◽  
Mengxue Li ◽  
Huan Li
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Adaboost Algorithm ◽  
Color Features

A Novel Approach of Face Detection Based on Skin Color Segmentation and SVM

2011 ◽  
Vol 225-226 ◽  
pp. 437-441
Author(s):  
Jing Zhang ◽  
You Li
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Information Access ◽  
Color Segmentation ◽  
Support Vector ◽  
Svm Classifier ◽  
Novel Approach ◽  
Great Performance ◽  
Face Detection And Recognition ◽  
Detection And Recognition

Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on skin color segmentation and Support Vector Machine(SVM) is proposed. Firstly, segmenting image using color model to filter candidate faces roughly; And then Eye-analogue segments at a given scale are discovered by finding regions which are darker than their neighborhoods to filter candidate faces farther; at the end, SVM classifier is used to detect face feature in the test image, SVM has great performance in classification task. Our tests in this paper are based on MIT face database. The experimental results demonstrate that the proposed method is encouraging with a successful detection rate.

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