文章基本信息

标题：Fourier Locally Linear Soft Constrained MACE for facial landmark localization
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作者：Wenming Yang ; Xiang Sun ; Weihong Deng 等
期刊名称：CAAI Transactions on Intelligence Technology
电子版ISSN：2468-2322
出版年度：2016
卷号：1
期号：3
页码：241-248
DOI：10.1016/j.trit.2016.10.006
出版社：IET Digital Library
摘要：This paper proposes a novel nonlinear correlation filter for facial landmark localization. Firstly, we prove that SVM as a classifier can also be used for localization. Then, soft constrained Minimum Average Correlation Energy filter (soft constrained MACE) is proposed, which is more resistent to overfittings to training set than other variants of correlation filter. In order to improve the performance for the multi-mode of the targets, locally linear framework is introduced to our model, which results in Fourier Locally Linear Soft Constraint MACE (FL2 SC-MACE). Furthermore, we formulate the fast implementation and show that the time consumption in test process is independent of the number of training samples. The merits of our method include accurate localization performance, desiring generalization capability to the variance of objects, fast testing speed and insensitivity to parameter settings. We conduct the cross-set eye localization experiments on challenging FRGC, FERET and BioID datasets. Our method surpasses the state-of-arts especially in pixelwise accuracy.
关键词：Facial landmark localization; Overfitting; Multimode; FL2 SC-MACE