.png){kind=icon}
返回
Image Set-Based Collaborative Representation for Face Recognition
.png){kind=icon}
OA
AI
DOI：10.1109/TIFS.2014.2324277.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the rapid development of digital imaging and communication technologies, image set-based face recognition (ISFR) is becoming increasingly important. One key issue of ISFR is how to effectively and efficiently represent the query face image set using the gallery face image sets. The set-to-set distance-based methods ignore the relationship between gallery sets, whereas representing the query set images individually over the gallery sets ignores the correlation between query set images. In this paper, we propose a novel image set-based collaborative representation and classification method for ISFR. By modeling the query set as a convex or regularized hull, we represent this hull collaboratively over all the gallery sets. With the resolved representation coefficients, the distance between the query set and each gallery set can then be calculated for classification. The proposed model naturally and effectively extends the image-based collaborative representation to an image set based one, and our extensive experiments on benchmark ISFR databases show the superiority of the proposed method to state-of-the-art ISFR methods under different set sizes in terms of both recognition rate and efficiency.
Keyword:
Collaborative representation
face recognition
image set
set to sets distance
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:
8
论文数:
5.2K
被引数:
2.3W
机构
引用论文
Kernel discriminant transformation for image set-based face recognition.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
分享收藏Cardiac Protection by Preconditioning Is Generated via an Iron-Signal Created by Proteasomal Degradation of Iron ProteinsPLoS ONEOAAI
PLoS ONEIF0
OAAI分享收藏
查看更多内容