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Journal of Electronic Imaging • new

Cascaded face alignment via intimacy definition feature
Author(s): Hailiang Li; Kin-Man Lam; Man-Yau Chiu; Kangheng Wu; Zhibin Lei
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Paper Abstract

Recent years have witnessed the emerging popularity of regression-based face aligners, which directly learn mappings between facial appearance and shape-increment manifolds. We propose a random-forest based, cascaded regression model for face alignment by using a locally lightweight feature, namely intimacy definition feature. This feature is more discriminative than the pose-indexed feature, more efficient than the histogram of oriented gradients feature and the scale-invariant feature transform feature, and more compact than the local binary feature (LBF). Experimental validation of our algorithm shows that our approach achieves state-of-the-art performance when testing on some challenging datasets. Compared with the LBF-based algorithm, our method achieves about twice the speed, 20% improvement in terms of alignment accuracy and saves an order of magnitude on memory requirement.

Paper Details

Date Published: 27 October 2017
PDF: 12 pages
J. Electron. Imag. 26(5) 053024 doi: 10.1117/1.JEI.26.5.053024
Published in: Journal of Electronic Imaging Volume 26, Issue 5
Show Author Affiliations
Hailiang Li, Hong Kong Polytechnic Univ. (Hong Kong)
Hong Kong Applied Science and Technology Research Institute Co. Ltd. (Hong Kong)
Kin-Man Lam, The Hong Kong Polytechnic Univ. (Hong Kong)
Man-Yau Chiu, Hong Kong Applied Science and Technology Research Institute Co. Ltd. (Hong Kong)
Kangheng Wu, Hong Kong Applied Science and Technology Research Institute Co. Ltd. (Hong Kong)
Zhibin Lei, Hong Kong Applied Science and Technology Research Institute Co. Ltd. (Hong Kong)


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