Abstract
Although important progresses have been already made in face detection, many false faces can be found in detection results and false detection rate is influenced by some factors, such as rotation and tilt of human face, complicated background, illumination, scale, cloak and hairstyle. This paper proposes a new method called DP-Adaboost algorithm to detect multi-angle human face and improve the correct detection rate. An improved Adaboost algorithm with the fusion of frontal face classifier and a profile face classifier is used to detect the multi-angle face. An improved horizontal differential projection algorithm is put forward to remove those non-face images among the preliminary detection results from the improved Adaboost algorithm. Experiment results show that compared with the classical Adaboost algorithm with a frontal face classifier, the textual DP-Adaboost algorithm can reduce false rate significantly and improve hit rate in multi-angle face detection.
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Ying-Ying Zheng is a master student in mechanical & electronic engineering and automation of Shanghai University, China.
Her research interests include image processing and machine vision.
ORCID iD: 0000-0002-9484-5113
Jun Yao received the B. Sc. degrees in industrial automation from the Shanghai University, China in 2000, received the Ph.D. degree in control theory and engineering from Shanghai University, China in 2006. Currently, he is a lecturer in the School of Mechanical & Electronic Engineering and Automation, Shanghai University, China. He has published about 20 refereed journals, conference papers and patents.
His research interests include machine vision, digital image processing, and signal processing in detecting system.
ORCID iD: 0000-0002-5438-077X
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Zheng, YY., Yao, J. Multi-angle face detection based on DP-Adaboost. Int. J. Autom. Comput. 12, 421–431 (2015). https://doi.org/10.1007/s11633-014-0872-8
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DOI: https://doi.org/10.1007/s11633-014-0872-8