中文说明：我们提出了一个统一的模型，用于人脸检测，姿态估计和地标估计在现实世界中，杂乱的图像。我们的模型是基于一个共享部分池的树的混合物；我们将每个面部地标建模为一个部分，并使用全局混合物来捕捉由于视点而引起的拓扑变化。我们证明了树结构模型在捕捉整体弹性变形方面出人意料地有效，同时与密集图结构不同，它易于优化。我们提供了标准人脸基准测试的大量结果，以及一个新的“野外”注释数据集，这表明我们的系统在所有三项任务中都取得了最新的进展，有时甚至相当大。尽管我们的模型经过了数百张人脸的适度训练，但它与经过数十亿个例子训练的商业系统（如Google Picasa和Google）相比还是有优势的face.com网站). 此文件显示完整的
English Description：
We present a unified model for face detection, pose estimation, and landmark estimation in real-world, cluttered images. Our model is based on a mixtures of trees with a shared pool of parts; we model every facial landmark as a part and use global mixtures to capture topological changes due to viewpoint. We show that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures. We present extensive results on standard face benchmarks, as well as a new "in the wild" annotated dataset, that suggests our system advances the state-of-the-art, sometimes considerably, for all three tasks. Though our model is modestly trained with hundreds of faces, it compares favorably to commercial systems trained with billions of examples (such as Google Picasa and face.com). Thid file presents a full