The “interpretation through synthesis” approach to analyze face images, particularly Active Appearance Models (AAMs) method, has become one of the most successful face modeling approaches over the last two decades. AAM models have ability to represent face images through synthesis using a controllable parameterized Principal Component Analysis (PCA) model. However, the accuracy and robustness of the synthesized faces of AAMs are highly depended on the training sets and inherently on the generalizability of PCA subspaces. This paper presents a novel Deep Appearance Models (DAMs) approach, an efficient replacement for AAMs, to accurately capture both shape and texture of face images under large variations. In this approach, three crucial components represented in hierarchical layers are modeled using the Deep Boltzmann Machines (DBM) to robustly capture the variations of facial shapes and appearances. DAMs are therefore superior to AAMs in inferencing a representation for new face images under various challenging conditions. The proposed approach is evaluated in various applications to demonstrate its robustness and capabilities, i.e. facial super-resolution reconstruction, facial off-angle reconstruction or face frontalization, facial occlusion removal and age estimation using challenging face databases, i.e. Labeled Face Parts in the Wild, Helen and FG-NET. Comparing to AAMs and other deep learning based approaches, the proposed DAMs achieve competitive results in those applications, thus this showed their advantages in handling occlusions, facial representation, and reconstruction.

中文翻译：

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深度外观模型：用于人脸建模的深度玻尔兹曼机方法

分析人脸图像的“通过合成解释”方法，特别是主动外观模型（AAMs）方法，已成为过去二十年最成功的人脸建模方法之一。AAM 模型能够通过使用可控参数化主成分分析 (PCA) 模型的合成来表示人脸图像。然而，AAM 合成人脸的准确性和鲁棒性高度依赖于训练集，并且本质上依赖于 PCA 子空间的泛化性。本文提出了一种新颖的深度外观模型 (DAM) 方法，它是 AAM 的有效替代品，可准确捕获大变化下的面部图像的形状和纹理。在这种方法中，使用深度玻尔兹曼机 (DBM) 对分层层中表示的三个关键组件进行建模，以稳健地捕捉面部形状和外观的变化。因此，在各种具有挑战性的条件下，DAM 在推断新人脸图像的表示方面优于 AAM。所提出的方法在各种应用中进行了评估，以证明其鲁棒性和能力，即面部超分辨率重建、面部偏角重建或面部正面化、面部遮挡去除和使用具有挑战性的面部数据库（即野外标记面部部位）进行年龄估计，海伦和 FG-NET。与 AAM 和其他基于深度学习的方法相比，所提出的 DAM 在这些应用中取得了有竞争力的结果，从而显示了它们在处理遮挡、面部表征、