Deformable Templates for Face Description, Recognition, Interpretation, and Learning

小行星9317670 这是一个为期三年的连续奖项的第一年资助，该奖项旨在通过制定新的建模和匹配算法来研究在光照，光照，几何形状和视角变化下的鲁棒人脸识别问题。 该方法包括衍生的模型集，插值时，用于处理广泛的变化的观点。 所使用的模型由两部分组成：第一，与空间变化和个人和表情变化相关的几何模型，第二，成像模型，用于处理照明和光照的变化。 研究涉及以下四个目标：1）设计、测试和实现算法，以将所描述的模型与正视的面部相匹配，2）用于跟踪面部及其特征的可变形模板技术的扩展，并根据表情确定解释性映射，3）发展概率方法学，用于首先从数据库学习可变形模板所使用的先验分布，并且随后，开发一种学习方法来确定模型的最大后验估计，以及4）将模型推广到在复杂的视觉场景中检测多个人脸，并且即使在存在离群值和遮挡物的情况下也鲁棒地执行。

9317670 Yuille This is the first year funding of a three-year continuing award to investigate the problems of robust face recognition under lighting, albedo, geometry and viewpoint variations by formulating new modeling and matching alogrithms. The approach consists of deriving sets of models which, when interpolated, serve to handle wide variations in viewpoint. The model used consists of two parts: first, a geometric model related to spatial variations and individual and expression changes, and second, an imaging model, which handles variations in lighting and albedo. The research involves the following four goals: 1) the design, test, and implementation of algorithms to match the model described against frontally-viewed faces, 2) the extension of deformable template techniques for tracking faces and their features and determine an interpretative mapping in terms of expressions, 3) the development of probabilistic methodologies for first learning from a database the prior distributions used by deformable templates, and later, to develop a learning method to determine the maximum a posteriori estimate of the model, and 4) to generalize the model to detect multiple faces in complex visual scenes, and perform robustly even in the presence of outliers and occluders.