Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75910
Title: Cascaded face alignment via intimacy definition feature
Authors: Li, HL 
Lam, KM 
Chiu, MY
Wu, KH
Lei, ZB
Keywords: Cascaded face alignment
Random forest
Intimacy definition feature
Issue Date: 2017
Publisher: SPIE-International Society for Optical Engineering
Source: Journal of electronic imaging, 2017, v. 26, no. 5, 53024 How to cite?
Journal: Journal of electronic imaging 
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 randomforest 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 LBFbased algorithm, our method achieves about twice the speed, 20% improvement in terms of alignment accuracy and saves an order of magnitude on memory requirement.
URI: http://hdl.handle.net/10397/75910
ISSN: 1017-9909
EISSN: 1560-229X
DOI: 10.1117/1.JEI.26.5.053024
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