.png){kind=icon}
返回
Multibiometric human recognition using 3D ear and face features
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2012.09.016.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present automatic extraction of local 3D features (L3DF) from ear and face biometrics and their combination at the feature and score levels for robust identification. To the best of our knowledge, this paper is the first to present feature level fusion of 3D features extracted from ear and frontal face data. Scores from L3DF based matching are also fused with iterative closest point algorithm based matching using a weighted sum rule. We achieve identification and verification (at 0.001 FAR) rates of 99.0% and 99.4%, respectively, with neutral and 96.8% and 97.1% with non-neutral facial expressions on the largest public databases of 3D ear and face. (C) 2012 Elsevier Ltd. All rights reserved.
Keyword:
3D face
Local 3D surface features
Multibiometric multimodal recognition
Score-level fusion
Feature-level fusion
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:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Evaluation of a Urine Cortisol:Creatinine Ratio as a Screening Test for Hyperadrenocorticism in Dogs
OAAI
.png){kind=icon}
OAAI分享收藏
查看更多内容