Localizing Parts of Faces Using a Consensus of Exemplars
Localizing Parts of Faces Using a Consensus of Exemplars
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DOI：
10.1109/tpami.2013.23
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发表时间：
2013-12-01
影响因子：
23.6
通讯作者：
Kumar, Neeraj
中科院分区：
文献类型：
--
作者：
Belhumeur, Peter N.;Jacobs, David W.;Kumar, Neeraj
我们提出了一种新的方法来定位人脸图像中的部分。该方法将局部检测器的输出与基于1，000多个手动标记样本图像的部件位置的非参数全局模型集相结合。通过假设全局模型生成的部分位置作为隐藏变量，我们推导出贝叶斯目标函数。该函数使用这些隐藏变量的一致性模型进行优化。与之前的定位器相比，生成的定位器处理的表情、姿势、照明和遮挡范围更广。我们在现实世界的人脸数据集上表现出了出色的性能，例如Labeled Faces in the Wild（LFW）和新的Labeled Face Parts in the Wild（LFPW），并表明我们的定位器在挑战性较小的BioID数据集上实现了最先进的性能。
We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a nonparametric set of global models for the part locations based on over 1,000 hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting, and occlusion than prior ones. We show excellent performance on real-world face datasets such as Labeled Faces in the Wild (LFW) and a new Labeled Face Parts in the Wild (LFPW) and show that our localizer achieves state-of-the-art performance on the less challenging BioID dataset.