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Gradient Shape Model
International Journal of Computer Vision ( IF 19.5 ) Pub Date : 2020-06-09 , DOI: 10.1007/s11263-020-01341-y
Pedro Martins , João F. Henriques , Jorge Batista

For years, the so-called Constrained Local Model (CLM) and its variants have been the gold standard in face alignment tasks. The CLM combines an ensemble of local feature detectors whose locations are regularized by a shape model. Fitting such a model typically consists of an exhaustive local search using the detectors and a global optimization that finds the CLM’s parameters that jointly maximize all the responses. However, one major drawback of CLMs is the inefficiency of the local search, which relies on a large amount of expensive convolutions. This paper introduces the Gradient Shape Model (GSM), a novel approach that addresses this limitation. We are able to align a similar CLM model without the need for any convolutions at all. We also use true analytical gradient and Hessian matrices, which are easy to compute, instead of their approximations. Our formulation is very general, allowing an optional 3D shape term to be seamlessly included. Additionally, we expand the GSM formulation through a cascade regression framework. This revised technique allows a substantially reduction in the complexity/dimensionality of the data term, making it possible to compute a denser, more accurate, regression step per cascade level. Experiments in several standard datasets show that our proposed models perform faster than state-of-the-art CLMs and better than recent cascade regression approaches.

中文翻译:

渐变形状模型

多年来,所谓的约束局部模型 (CLM) 及其变体一直是人脸对齐任务的黄金标准。CLM 结合了一组局部特征检测器,其位置由形状模型正则化。拟合这样的模型通常包括使用检测器的详尽局部搜索和全局优化,该优化找到联合最大化所有响应的 CLM 参数。然而,CLM 的一个主要缺点是局部搜索效率低下,这依赖于大量昂贵的卷积。本文介绍了梯度形状模型 (GSM),这是一种解决此限制的新方法。我们能够在完全不需要任何卷积的情况下对齐类似的 CLM 模型。我们还使用易于计算的真解析梯度和 Hessian 矩阵,而不是它们的近似值。我们的公式非常通用,允许无缝包含可选的 3D 形状项。此外,我们通过级联回归框架扩展了 GSM 公式。这种修改后的技术可以显着降低数据项的复杂性/维度,从而可以计算每个级联级别的更密集、更准确的回归步骤。在几个标准数据集中的实验表明,我们提出的模型比最先进的 CLM 执行得更快,并且比最近的级联回归方法更好。这种修改后的技术可以显着降低数据项的复杂性/维度,从而可以计算每个级联级别的更密集、更准确的回归步骤。在几个标准数据集中的实验表明,我们提出的模型比最先进的 CLM 执行得更快,并且比最近的级联回归方法更好。这种修改后的技术可以显着降低数据项的复杂性/维度,从而可以计算每个级联级别的更密集、更准确的回归步骤。在几个标准数据集中的实验表明,我们提出的模型比最先进的 CLM 执行得更快,并且比最近的级联回归方法更好。
更新日期:2020-06-09
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