A Modified Adaboost Algorithm to Reduce False Positives in Face Detection

Cesar Niyomugabo, Hyo-rim Choi and Tae Yong Kim

Mathematical Problems in Engineering, 2016, vol. 2016, 1-6

Abstract:

We present a modified Adaboost algorithm in face detection, which aims at an accurate algorithm to reduce false-positive detection rates. We built a new Adaboost weighting system that considers the total error of weak classifiers and classification probability. The probability was determined by computing both positive and negative classification errors for each weak classifier. The new weighting system gives higher weights to weak classifiers with the best positive classifications, which reduces false positives during detection. Experimental results reveal that the original Adaboost and the proposed method have comparable face detection rate performances, and the false-positive results were reduced almost four times using the proposed method.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:5289413

DOI: 10.1155/2016/5289413

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