NDLI: Robust virtual frontal face synthesis from a given pose using regularized linear regression

Content Provider	IEEE Xplore Digital Library
Author	Hao, Yuanhong Qi, Chun
Copyright Year	2014
Description	Author affiliation: School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China (Hao, Yuanhong; Qi, Chun)
Abstract	Locally linear regression (LLR) is a simple yet efficient algorithm for synthesizing a virtual frontal image from a nonfrontal viewpoint. However, the effect of LLR is impacted by the patch size. Moreover, for the adjacent patches of the nose and mouth part, different local linear mappings cannot guarantee the predicted virtual frontal patches to be harmonious. The major cause is the different local geometric shape for different person. To overcome or at least to reduce the problem of LLR, we propose a regularization framework by introducing a global regularization item into the original local regression object function. The reconstruction weights are estimated through the new model and the virtual frontal face are predicted using the weights. Experimental results show that the method performs better than the LLR.
Starting Page	4702
Ending Page	4706
File Size	605142
Page Count	5
File Format	PDF
ISBN	9781479957514
DOI	10.1109/ICIP.2014.7025953
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-27
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face Linear regression PSNR Training Robustness Image reconstruction Face recognition virtual frontal view Regularization local patch linear regression
Content Type	Text
Learning Resource Type	Article

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