Volume 9 Number 5 (May 2014)
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JCP 2014 Vol.9(5): 1077-1082 ISSN: 1796-203X
doi: 10.4304/jcp.9.5.1077-1082

Adaboost Face Detection Based on Improved Covariance Feature

Rui Li, Changfeng Li
Lanzhou University of Technology//School of Computer and Communication, Lanzhou, China

Abstract—Excessive number of Haar-like features and the complex threshold calculation of covariance matrix feature are two key issues of Adaboost face detection. In this paper, an efficient feature named covariance feature is proposed. The novel method divides the face image into several regions and it calculate covariance feature of any two regions. Then optimal weak classifiers will be picked out by Adaboost algorithm and they will be composed to a strong classifier. The experiments result in MIT+CMU data sets shows that the feature extraction times of the novel method is slightly slower than covariance matrix feature. However, the feature threshold is obtained much faster than covariance matrix feature, leading the significant reduction of the training time of Adaboost algorithm. Comparing with the Haar-like feature, the detection rate and speed improved obviously.

Index Terms—face detection, covariance feature, Adaboost, feature extraction

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Cite: Rui Li, Changfeng Li, "Adaboost Face Detection Based on Improved Covariance Feature," Journal of Computers vol. 9, no. 5, pp. 1077-1082, 2014.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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