Informative frequent assembled feature for face detection

Authors:
Bang Zhang;Getian Ye;Yang Wang;Wei Wang;Jie Xu;Gunawan Herman;Jun Yang
Affiliations:
National ICT Australia and School of Computer Science and Engineering, The University of New South Wales, Australia;National ICT Australia and School of Computer Science and Engineering, The University of New South Wales, Australia;National ICT Australia and School of Computer Science and Engineering, The University of New South Wales, Australia;National ICT Australia and School of Computer Science and Engineering, The University of New South Wales, Australia;National ICT Australia and School of Computer Science and Engineering, The University of New South Wales, Australia;National ICT Australia and School of Computer Science and Engineering, The University of New South Wales, Australia;National ICT Australia and School of Computer Science and Engineering, The University of New South Wales, Australia
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 3
Cited 0

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Abstract

In this paper, we propose a novel approach to automatically generating, instead of manually designing, discriminative visual features for face detection. The features are composed by multiple local features (e.g., Haar features), and such features can capture not only the local texture information but also their spatial configurations. Therefore, the proposed feature contains rich semantic information so that the classifier built on a set of such features can achieve high accuracy and high efficiency. Experimental results show that the proposed approach outperforms the techniques based on local features and the state-of-the-art discriminative features for face detection.