The Adaboost Algorithm Applied in ATM Automatic Identification System

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Abstract:

Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

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Periodical:

Advanced Materials Research (Volumes 753-755)

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2941-2944

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Freund Y, Schapire R E. Experiments with a new boosting algorithm [J]. Proc. the 13th Conf. Machine Learning, San Francisco: Morgan Kaufmann, 1996, 148-156.

Google Scholar

[2] R. E. Schapire. Y. Singer. Improved boosting algorithms using confidencerated predictions [J]. Machine Learning, 1999. 37(3): 297-336.

Google Scholar

[3] Viola P. Jones M, Rapid object detection using a boosted cascade of simple features[A]. IEEE Conference on CVPR'2001[C]. Lihue, Kauai, Hawaii, USA, IEEE Computer Society Press, 2001. 511-518.

DOI: 10.1109/cvpr.2001.990517

Google Scholar

[4] Yan Yunxiang, Guo Zhibo, Yang Jingyu. Fast face detection using AdaBoost Algorithm based feature value division [J]. Journal of Chinese Computer Systems, 2007(28)11: 2106-2109.

Google Scholar