[1]CHEN Chun-yan,ZHANG Pin-zheng,LUO Li-min.Face detection using real Adaboost on granular features[J].CAAI Transactions on Intelligent Systems,2009,4(5):446-452.[doi:10.3969/j.issn.1673-4785.2009.05.010]
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Face detection using real Adaboost on granular features

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 4 Number of periods: 2009 5 Page number: 446-452 Column: 学术论文—机器感知与模式识别 Public date: 2009-10-25
Title:
Face detection using real Adaboost on granular features
Author(s):
CHEN Chun-yan;  ZHANG Pin-zheng;  LUO Li-min
Image Science and Technology Laboratory, Southeast University, Nanjing 210096, China
Keywords:
granular features;  Bayesian stump;  real Adaboost;  boosting cascade;  face detection
CLC:
TP391.41
DOI:
10.3969/j.issn.1673-4785.2009.05.010
Abstract:
A face detection method based on sparse granular features and the real adaptive boosting (Adaboost) meta-algorithm was proposed. A sparse granular feature set was introduced into the Adaboost learning framework. A weak look-up-table (LUT) type classifier with real confidence output was designed by extending the Bayesian stump. Then, the space of the weak classifier was constructed. The Adaboost cascade face detector was taught by using a large training set and an evaluation set. Experiments were performed on the CMU-MIT dataset, a standard public data set for benchmarking frontal face detection systems. The detection rate reached over 90% when false alarms were 20. The average processing time on a Pentium Dual1.2GHz PC was about 100 ms for a 320×240pixel image. This shows the proposed method provides good precision and speed.
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