Department of Computer and Information Science and Engineering, University of Florida, Gainesville, 32611, USA.
IEEE Trans Pattern Anal Mach Intell. 2011 Mar;33(3):553-67. doi: 10.1109/TPAMI.2010.67.
Modeling illumination effects and pose variations of a face is of fundamental importance in the field of facial image analysis. Most of the conventional techniques that simultaneously address both of these problems work with the Lambertian assumption and thus fall short of accurately capturing the complex intensity variation that the facial images exhibit or recovering their 3D shape in the presence of specularities and cast shadows. In this paper, we present a novel Tensor-Spline-based framework for facial image analysis. We show that, using this framework, the facial apparent BRDF field can be accurately estimated while seamlessly accounting for cast shadows and specularities. Further, using local neighborhood information, the same framework can be exploited to recover the 3D shape of the face (to handle pose variation). We quantitatively validate the accuracy of the Tensor Spline model using a more general model based on the mixture of single-lobed spherical functions. We demonstrate the effectiveness of our technique by presenting extensive experimental results for face relighting, 3D shape recovery, and face recognition using the Extended Yale B and CMU PIE benchmark data sets.
在面部图像分析领域，对面部光照效果和姿态变化进行建模具有重要意义。大多数同时解决这两个问题的传统技术都基于朗伯假设，因此无法准确捕捉面部图像所呈现的复杂强度变化，或者在存在镜面反射和投影阴影的情况下恢复其 3D 形状。本文提出了一种新的基于张量样条的面部图像分析框架。我们表明，使用该框架可以在无缝处理投影阴影和镜面反射的同时，准确估计面部表观 BRDF 场。此外，利用局部邻域信息，还可以利用相同的框架恢复面部的 3D 形状（以处理姿态变化）。我们使用基于单叶球形函数混合的更通用模型来定量验证张量样条模型的准确性。我们通过使用扩展耶鲁 B 和 CMU PIE 基准数据集进行面部重光照、3D 形状恢复和人脸识别的广泛实验结果来证明我们技术的有效性。
