Modeling shape and textural variations in aging faces
Modeling shape and textural variations in aging faces
复制标题
DOI：
10.1109/afgr.2008.4813337
复制
发表时间：
2008-09
期刊：
影响因子：
--
通讯作者：
Narayanan Ramanathan;R. Chellappa
中科院分区：
文献类型：
--
作者：
Narayanan Ramanathan;R. Chellappa
我们提出了一个双重的方法对成人面部老化建模。首先，我们开发了一个形状变换模型，该模型被制定为基于物理的参数化肌肉模型，该模型捕获面部特征随年龄变化而发生的细微变形。该模型隐含地考虑了个体面部肌肉的物理特性和几何方向。接下来，我们开发了一个基于图像梯度的纹理变换函数，该函数表征了在不同年龄期间经常观察到的面部皱纹和其他皮肤伪影。面部生长统计（包括形状和纹理）在开发上述转换模型中起着至关重要的作用。从一个数据库，包括对年龄分离的人脸图像的许多人，我们提取基于年龄的面部测量的关键基准功能，并进一步研究纹理变化的年龄。我们提出的实验结果，说明了所提出的面部老化模型的应用，如人脸识别和面部外观预测跨老化的任务。
We propose a two fold approach towards modeling facial aging in adults. Firstly, we develop a shape transformation model that is formulated as a physically-based parametric muscle model that captures the subtle deformations facial features undergo with age. The model implicitly accounts for the physical properties and geometric orientations of the individual facial muscles. Next, we develop an image gradient based texture transformation function that characterizes facial wrinkles and other skin artifacts often observed during different ages. Facial growth statistics (both in terms of shape and texture) play a crucial role in developing the aforementioned transformation models. From a database that comprises of pairs of age separated face images of many individuals, we extract age-based facial measurements across key fiducial features and further, study textural variations across ages. We present experimental results that illustrate the applications of the proposed facial aging model in tasks such as face recognition and facial appearance prediction across aging.