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Regularized Transfer Boosting for Face Detection Across Spectrum | IEEE Journals & Magazine | IEEE Xplore

Regularized Transfer Boosting for Face Detection Across Spectrum


Abstract:

This letter addresses the problem of face detection in multispectral illuminations. Face detection in visible images has been well addressed based on the large scale trai...Show More

Abstract:

This letter addresses the problem of face detection in multispectral illuminations. Face detection in visible images has been well addressed based on the large scale training samples. For the recently emerging multispectral face biometrics, however, the face data is scarce and expensive to collect, and it is usually short of face samples to train an accurate face detector. In this letter, we propose to tackle the issue of multispectral face detection by combining existing large scale visible face images and a few multispectral face images. We cast the problem of face detection across spectrum into the transfer learning framework and try to learn the robust multispectral face detector by exploring relevant knowledge from visible data domain. Specifically, a novel Regularized Transfer Boosting algorithm named R-TrBoost is proposed, with features of weighted loss objective and manifold regularization. Experiments are performed with face images of two spectrums, 850 nm and 365 nm, and the results show significant improvement on multispectral face detection using the proposed algorithm.
Published in: IEEE Signal Processing Letters ( Volume: 19, Issue: 3, March 2012)
Page(s): 131 - 134
Date of Publication: 18 October 2011

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