.png){kind=icon}
返回
Kernel discriminant transformation for image set-based face recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2011.02.011.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This study presents a novel kernel discriminant transformation (KDT) algorithm for face recognition based on image sets. As each image set is represented by a kernel subspace, we formulate a KDT matrix that maximizes the similarities of within-kernel subspaces, and simultaneously minimizes those of between-kernel subspaces. Although the KDT matrix cannot be computed explicitly in a high-dimensional feature space, we propose an iterative kernel discriminant transformation algorithm to solve the matrix in an implicit way. Another perspective of similarity measure, namely canonical difference, is also addressed for matching each pair of the kernel subspaces, and employed to simplify the formulation. The proposed face recognition system is demonstrated to outperform existing still-image-based as well as image set-based face recognition methods using the Yale Face database B, Labeled Faces in the Wild and a self-compiled database. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Subspace method
Canonical correlation
Kernel discriminant analysis
Face recognition
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
NMR quantification of diffusional exchange in cell suspensions with relaxation rate differences between intra and extracellular compartments.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Nuclear magnetic resonance investigation of the helix to random coil transformation in poly‐α‐amino acids. I. Poly‐L‐alanineBiopolymers
BiopolymersIF0
PREAI
分享收藏
Subspace distance analysis with application to adaptive Bayesian algorithm for face recognitionPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Evaluation of charge transfer resistance by geometrical extrapolation of the centre of semicircular impedance diagrams通过半圆阻抗图中心的几何外推法评估电荷转移电阻
PREAI
分享收藏
查看更多内容