Open AccessAnalysis of face detection based on skin color characteristic and AdaBoost algorithm
Author(s) -
Pei Li,
Hongjuan Wang,
Yeli Li,
Mengyang Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1601/5/052019
Subject(s) - face detection , artificial intelligence , computer science , adaboost , skin color , pattern recognition (psychology) , face (sociological concept) , object class detection , computer vision , viola–jones object detection framework , classifier (uml) , feature (linguistics) , facial recognition system , linguistics , philosophy , social science , sociology
For the face detection method based on skin color feature and AdaBoost algorithm, if one of them is used to detect the face, it can also catch the face to a certain extent. However, the detection rate and an error rate of this single method in its detection experiment can’t achieve good results. Therefore, this paper combines the advantages of the two algorithms, combines the two approaches, and improves them. The main idea is to use the skin color features of face detection as pre-detection, and use the established skin color distribution Gaussian model to obtain candidate regions containing the skin color of the face, and then use a cascade classifier to detect the skin color regions. By using OpenCV and Visual Studio software, a lot of experimental statistics and analysis are carried out. The research shows that the improved algorithm is superior to the two algorithms in detection rate and false detection rate, and it can also achieve a good detection effect for the face in a complicated situation.