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A Functional Regression Approach to Facial Landmark Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence ( IF 23.6 ) Pub Date : 2017-08-29 , DOI: 10.1109/tpami.2017.2745568
Enrique Sanchez-Lozano , Georgios Tzimiropoulos , Brais Martinez , Fernando De la Torre , Michel Valstar

Linear regression is a fundamental building block in many face detection and tracking algorithms, typically used to predict shape displacements from image features through a linear mapping. This paper presents a Functional Regression solution to the least squares problem, which we coin Continuous Regression, resulting in the first real-time incremental face tracker. Contrary to prior work in Functional Regression, in which B-splines or Fourier series were used, we propose to approximate the input space by its first-order Taylor expansion, yielding a closed-form solution for the continuous domain of displacements. We then extend the continuous least squares problem to correlated variables, and demonstrate the generalisation of our approach. We incorporate Continuous Regression into the cascaded regression framework, and show its computational benefits for both training and testing. We then present a fast approach for incremental learning within Cascaded Continuous Regression, coined iCCR, and show that its complexity allows real-time face tracking, being 20 times faster than the state of the art. To the best of our knowledge, this is the first incremental face tracker that is shown to operate in real-time. We show that iCCR achieves state-of-the-art performance on the 300-VW dataset, the most recent, large-scale benchmark for face tracking.

中文翻译:

基于功能回归的人脸地标跟踪方法

线性回归是许多人脸检测和跟踪算法中的基本构建块,通常用于通过线性映射来预测图像特征的形状位移。本文提出了针对最小二乘问题的函数回归解决方案,我们将其称为连续回归,从而产生了第一个实时增量面部跟踪器。与使用B样条或傅里叶级数的功能回归的先前工作相反,我们建议通过一阶泰勒展开来近似输入空间,从而为位移的连续域提供封闭形式的解决方案。然后,我们将连续最小二乘问题扩展到相关变量,并证明了我们方法的一般性。我们将持续回归合并到级联回归框架中,并展示其在训练和测试方面的计算优势。然后,我们提出了一种在级联连续回归(简称为iCCR)中进行增量学习的快速方法,并证明了其复杂性可以实现实时人脸跟踪,其速度是最新技术的20倍。据我们所知,这是第一个实时显示操作的增量式面部跟踪器。我们展示了iCCR在300-VW数据集上实现了最先进的性能,这是最新的大规模面部跟踪基准。这是第一个实时显示的增量面部跟踪器。我们展示了iCCR在300-VW数据集上实现了最先进的性能,这是最新的大规模面部跟踪基准。这是第一个实时显示的增量面部跟踪器。我们展示了iCCR在300-VW数据集上实现了最先进的性能,这是最新的大规模面部跟踪基准。
更新日期:2018-08-06
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