Learning deformable shape manifolds

PATTERN RECOGNITION

卷:45

Article

Rivera, Samuel¹  Martinez, Aleix M.^1,2 

[1] Ohio State Univ, CBCSL, Columbus, OH 43210 USA

[2] Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA

关键词: Shape modeling;    Detailed face shape detection;    Face detection;    Nonlinear regression;    Face recognition;    Manifold learning;   

DOI  :  10.1016/j.patcog.2011.09.023

来源: Elsevier

【 摘 要 】

We propose an approach to shape detection of highly deformable shapes in images via manifold learning with regression. Our method does not require shape key points be defined at high contrast image regions, nor do we need an initial estimate of the shape. We only require sufficient representative training data and a rough initial estimate of the object position and scale. We demonstrate the method for face shape learning, and provide a comparison to nonlinear Active Appearance Model. Our method is extremely accurate, to nearly pixel precision and is capable of accurately detecting the shape of faces undergoing extreme expression changes. The technique is robust to occlusions such as glasses and gives reasonable results for extremely degraded image resolutions. (C) 2011 Elsevier Ltd. All rights reserved.

【 授权许可】

Free