Efficient Facial Expression Recognition Using Adaboost and Haar Cascade Classifiers

被引：0
|
作者：
Gudipati, Vamshi Krishna [1 ]
Barman, Oindrila Ray [1 ]
Gaffoor, Mofica [1 ]
Harshagandha [1 ]
Abuzneid, Abdelshakour [1 ]
机构：
[1] Univ Bridgeport, Dept Comp Sci & Engn, Bridgeport, CT 06604 USA
来源：
2016 ANNUAL CONNECTICUT CONFERENCE ON INDUSTRIAL ELECTRONICS, TECHNOLOGY AND AUTOMATION (CT-IETA) | 2016年
关键词：
Facial Expression Recognition; Adaboost; Haar Cascades; mouth detection; logistic regression; !text type='python']python[!/text] with openCV;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Image processing frameworks are focusing towards the use of computer vision techniques in human PC collaboration and feeling investigation through a space mapping between the constant feeling and an arrangement of discrete feeling classes. While accomplishing great execution, the most productive component space and characterization system for Face Expression Recognition (FER) stay obscure because of absence of correlation study. The Adaboost Algorithm is quickly clarified and executed in our program. This study enhances the acknowledgment exactness and the execution time of facial expression recognition framework.
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页数：4
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