A Modified Adaboost Algorithm to Reduce False Positives in Face Detection. (28th September 2016)

• Record Type: Journal Article
• Authors: Niyomugabo, Cesar; Choi, Hyo-rim; Kim, Tae Yong
• Other Names: Liang Jinyang Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2016(2016)
• Publication Date: 2016-09-28
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2016/5289413
• Languages: English
• ISSNs: 1024-123X

Abstract

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.