AAM fitting using shape parameter distribution

Youhei Shiraishi, S. Fujie, Tetsunori Kobayashi
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Abstract

A novel constraint using shape parameter distribution into the AAM fitting method is proposed. Active appearance models (AAMs) are some of the most popular facial models. AAM-based face tracking delivers accurate alignment results. However, non-face-like shapes can also be estimated by AAMs, unlike by the conventional AAM fitting method, which only minimizes the matching error of the image. This is one of the causes for face tracking performance degradation in AAMs. A constraint using the shape parameter distribution is added in order to solve this problem.
形状参数分布的AAM拟合
提出了一种将形状参数分布约束引入AAM拟合方法的新方法。主动外观模型(aam)是一些最流行的面部模型。基于aam的人脸跟踪提供准确的对齐结果。然而,与传统的AAM拟合方法不同,AAM也可以估计非人脸形状,而传统的AAM拟合方法只能将图像的匹配误差最小化。这是导致aam人脸跟踪性能下降的原因之一。为了解决这一问题,增加了形状参数分布约束。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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