A Face Detection Method Combining Improved AdaBoost Algorithm and Template Matching in Video Sequence

2016 ◽  
Author(s):  
Siyang Yan ◽  
Haiying Wang ◽  
Zhao Fang ◽  
Chan Wang
Keyword(s):  
Face Detection ◽  
Template Matching ◽  
Video Sequence ◽  
Detection Method ◽  
Adaboost Algorithm

Fast Face Detection Based on AdaBoost and Canny Operators

2014 ◽  
Vol 716-717 ◽  
pp. 936-939
Author(s):  
Lin Zhang
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
Face Image ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Human Face ◽  
Adaboost Algorithm ◽  
Amount Of Information ◽  
Detection Speed

Detection speed of traditional face detection method based on AdaBoost algorithm is slow since AdaBoost asks a large number of features. Therefore, to address this shortcoming, we proposed a fast face detection method based on AdaBoost and canny operators in this paper. Firstly, we use canny operators to detect edge of face image which separates the region of the possible human face from image, and then do face detection in the separated region using Modest AdaBoost algorithm (MAB). Before using MAB to achieve face detection, utilizing canny operators to detect edge can make this algorithm effectively filter information, retain useful information, reduce the amount of information and improve detection speed. Experimental results show that the algorithm can obtain higher detection accuracy and detection speed has been significantly improved at the same time.

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.

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

Real AdaBoost face detection method based on multi-threshold

2009 ◽  
Vol 29 (8) ◽  
pp. 2098-2100
Author(s):  
Shi-ming SUN ◽  
Qing PAN ◽  
You-fang JI
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
Real Adaboost
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