Illumination Invariant Face Detection Using Classifier Fusion

Authors:
Alister Cordiner;Philip Ogunbona;Wanqing Li
Affiliations:
CVIPCM, University of Wollongong, Wollongong, Australia and Digisensory Technologies, Sydney, Australia;CVIPCM, University of Wollongong, Wollongong, Australia;CVIPCM, University of Wollongong, Wollongong, Australia
Venue:
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Year:
2008

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Abstract

An approach to the problem of illumination variations in face detection that uses classifier fusion is presented. Multiple face detectors are seperately trained for different illumination environments and their results are combined using a combination rule. To define the illumination environments, the training samples are clustered based on their illumination using unsupervised training. Different methods of clustering the samples and combining the outputs of the classifiers are examined. Experiments with the AR face database show that the proposed method achieves higher accuracy than the traditional monolithic face detection method.