Suppr超能文献

用于非刚性面部对齐的高效约束局部模型拟合

Efficient Constrained Local Model Fitting for Non-Rigid Face Alignment.

作者信息

Lucey Simon, Wang Yang, Cox Mark, Sridharan Sridha, Cohn Jeffery F

机构信息

Robotics Institute, Carnegie Mellon University, Pittsburgh PA 15213, USA.

出版信息

Image Vis Comput. 2009 Nov 1;27(12):1804-1813. doi: 10.1016/j.imavis.2009.03.002.

Abstract

Active appearance models (AAMs) have demonstrated great utility when being employed for non-rigid face alignment/tracking. The "simultaneous" algorithm for fitting an AAM achieves good non-rigid face registration performance, but has poor real time performance (2-3 fps). The "project-out" algorithm for fitting an AAM achieves faster than real time performance (> 200 fps) but suffers from poor generic alignment performance. In this paper we introduce an extension to a discriminative method for non-rigid face registration/tracking referred to as a constrained local model (CLM). Our proposed method is able to achieve superior performance to the "simultaneous" AAM algorithm along with real time fitting speeds (35 fps). We improve upon the canonical CLM formulation, to gain this performance, in a number of ways by employing: (i) linear SVMs as patch-experts, (ii) a simplified optimization criteria, and (iii) a composite rather than additive warp update step. Most notably, our simplified optimization criteria for fitting the CLM divides the problem of finding a single complex registration/warp displacement into that of finding N simple warp displacements. From these N simple warp displacements, a single complex warp displacement is estimated using a weighted least-squares constraint. Another major advantage of this simplified optimization lends from its ability to be parallelized, a step which we also theoretically explore in this paper. We refer to our approach for fitting the CLM as the "exhaustive local search" (ELS) algorithm. Experiments were conducted on the CMU Multi-PIE database.

摘要

主动外观模型(AAMs)在用于非刚性面部对齐/跟踪时已展现出巨大的实用性。用于拟合AAM的“同时”算法实现了良好的非刚性面部配准性能,但实时性能较差(2 - 3帧/秒)。用于拟合AAM的“投影出”算法实现了高于实时的性能(> 200帧/秒),但一般对齐性能较差。在本文中,我们介绍了一种对用于非刚性面部配准/跟踪的判别方法的扩展,称为约束局部模型(CLM)。我们提出的方法能够在实现实时拟合速度(35帧/秒)的同时,取得优于“同时”AAM算法的性能。为了获得这种性能,我们通过多种方式改进了标准的CLM公式,包括:(i)使用线性支持向量机作为面片专家,(ii)简化优化标准,以及(iii)采用复合而非加法的变形更新步骤。最值得注意的是,我们用于拟合CLM的简化优化标准将寻找单个复杂配准/变形位移的问题分解为寻找N个简单变形位移的问题。从这N个简单变形位移中,使用加权最小二乘约束估计单个复杂变形位移。这种简化优化的另一个主要优点在于其能够并行化,我们在本文中也从理论上探讨了这一步骤。我们将我们拟合CLM的方法称为“穷举局部搜索”(ELS)算法。在CMU Multi - PIE数据库上进行了实验。

相似文献

3
Multi-View AAM Fitting and Construction.多视图主动形状模型拟合与构建
Int J Comput Vis. 2008 Feb 1;76(2):183-204. doi: 10.1007/s11263-007-0050-3.
5
Enforcing Convexity for Improved Alignment with Constrained Local Models.通过强制凸性来改进与约束局部模型的对齐。
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2008 Jun 23;2008:1-8. doi: 10.1109/CVPR.2008.4587808.
8
Fast and Exact Newton and Bidirectional Fitting of Active Appearance Models.主动外观模型的快速精确牛顿法和双向拟合
IEEE Trans Image Process. 2017 Feb;26(2):1040-1053. doi: 10.1109/TIP.2016.2642828. Epub 2016 Dec 21.
9
Efficient parallel implementation of active appearance model fitting algorithm on GPU.
ScientificWorldJournal. 2014 Mar 2;2014:528080. doi: 10.1155/2014/528080. eCollection 2014.
10
Least Squares Congealing for Unsupervised Alignment of Images.用于图像无监督对齐的最小二乘凝聚法
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2008 Jun 23;2008:1-8. doi: 10.1109/CVPR.2008.4587573.

引用本文的文献

本文引用的文献

1
Enforcing Convexity for Improved Alignment with Constrained Local Models.通过强制凸性来改进与约束局部模型的对齐。
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2008 Jun 23;2008:1-8. doi: 10.1109/CVPR.2008.4587808.
2
Multi-PIE.多姿态、光照和表情数据库
Proc Int Conf Autom Face Gesture Recognit. 2010 May 1;28(5):807-813. doi: 10.1016/j.imavis.2009.08.002.
3
Active shape model segmentation with optimal features.具有最优特征的主动形状模型分割
IEEE Trans Med Imaging. 2002 Aug;21(8):924-33. doi: 10.1109/TMI.2002.803121.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验