Lucey Simon, Wang Yang, Cox Mark, Sridharan Sridha, Cohn Jeffery F
Robotics Institute, Carnegie Mellon University, Pittsburgh PA 15213, USA.
Image Vis Comput. 2009 Nov 1;27(12):1804-1813. doi: 10.1016/j.imavis.2009.03.002.
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.
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。