.png){kind=icon}
返回
Probabilistic learning for fully automatic face recognition across pose
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2009.07.008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent pose invariant methods try to model the subject specific appearance change across pose. For this, however, almost all of the existing methods require a perfect alignment between a gallery and a probe image. In this paper we present a pose invariant face recognition method that does not require the facial landmarks to be detected as such and is able to work with only single training image of the subject. We propose novel extensions by introducing to use a more robust feature description as opposed to pixel-based appearances. Using such features we put forward to synthesize the non-frontal views to frontal. Furthermore, using local kernel density estimation, instead of commonly used normal density assumption, is suggested to derive the prior models. Our method does not require any strict alignment between gallery and probe images which makes it particularly attractive as compared to the existing state of the art methods. Improved recognition across a wide range of poses has been achieved using these extensions. (C) 2009 Elsevier B.V. All rights reserved.
Keyword:
Face recognition
Recognition across pose
Bayesian face modeling
Face-GLOH-Signature
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.2
论文数:
4.1K
被引数:
6.7K
机构
引用论文
Assessing climate change tolerance and the niche breadth-range size hypothesis in rare and widespread alpine plants.png)
Oecologia
OecologiaIF0
PREAI
分享收藏分享收藏分享收藏
查看更多内容