Fast Feature Value Searching for Face Detection | Zendy

Yunyang Yan | Zendy; Zhibo Guo | Zendy; Jingyu Yang | Zendy

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Fast Feature Value Searching for Face Detection

Author(s) -

Yunyang Yan,

Zhibo Guo,

Jingyu Yang

Publication year - 2008

Publication title -

computer and information science

Language(s) - English

Resource type - Journals

eISSN - 1913-8997

pISSN - 1913-8989

DOI - 10.5539/cis.v1n2p120

Subject(s) - computer science , adaboost , face detection , artificial intelligence , pattern recognition (psychology) , feature (linguistics) , face (sociological concept) , set (abstract data type) , detector , facial recognition system , telecommunications , social science , linguistics , philosophy , sociology , classifier (uml) , programming language

It would cost much and much time in face detector training using AdaBoost algorithm. An improved face detection algorithm called Rank-AdaBoost based on feature-value-division and Dual-AdaBoost based on dual-threshold are proposed to accelerate the training and improve detection performance. Using the improved AdaBoost, the feature values with respect to each Haar-like feature are rearrange to a definite number of ranks.The number of ranks is much less than that of the training samples, so that the test time on each training samples is saved corresponding to the original AdaBoost algorithm. Inheriting cascaded frame is also proposed here. Experimental results on MIT-CBCL face & nonface training data set illustrate that the improved algorithm could make training process convergence quickly and the training time is only one of 50 like before. Experimental results on MIT+CMU face set also show that the detection speed and accuracy are both better than the original method.

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