An Improved AdaBoost Algorithm

Shao Wen Liao; Yong Chen

doi:10.4028/www.scientific.net/AMR.1049-1050.1703

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1049-1050An Improved AdaBoost Algorithm

An Improved AdaBoost Algorithm

Abstract:

In view of the higher mistaken-detection rate problem of the human face detection in complex conditions, we put forward an improved algorithm. This article Proposes one kind of the method Which unifies the level of difference between the threshold and the feature value of the weak classifier with the weak classifier's overall error rate. Compared to the method which only based on the overall classification error rate to update the weights, this method can achieve higher detection rate while reduces the mistaken-detection rate. This article redefines the training error which is caused When we train the weak classifier, and Proposes MCE-AdaBoost algorithm. The new definition of training error will pay more attention to the error Which erroneously estimates the face Sample as non-face sample; this much more conforms to face detection of this special target detection issue. The experimental results show that MCE-AdaBoost algorithm can effectively improve the detection Performance of the final classifier.

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Periodical:

Advanced Materials Research (Volumes 1049-1050)

Pages:

1703-1706

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1049-1050.1703

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Online since:

October 2014

Authors:

Shao Wen Liao*, Yong Chen

Keywords:

Adaboost Algorithm, Cascaded Classifier, Face Detection, Integral Image, Strong Classifier, Weak Classifier

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