.png){kind=icon}
返回
Single-Sample Face Recognition Based on Intra-Class Differences in a Variation Model
.png){kind=icon}
OA
AI
DOI：10.3390/s150101071.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a novel random facial variation modeling system for sparse representation face recognition is presented. Although recently Sparse Representation-Based Classification (SRC) has represented a breakthrough in the field of face recognition due to its good performance and robustness, there is the critical problem that SRC needs sufficiently large training samples to achieve good performance. To address these issues, we challenge the single-sample face recognition problem with intra-class differences of variation in a facial image model based on random projection and sparse representation. In this paper, we present a developed facial variation modeling systems composed only of various facial variations. We further propose a novel facial random noise dictionary learning method that is invariant to different faces. The experiment results on the AR, Yale B, Extended Yale B, MIT and FEI databases validate that our method leads to substantial improvements, particularly in single-sample face recognition problems.
Keyword:
intra-class variation model differences
face recognition
sparse representation
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:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
.png){kind=icon}
Regularized Kernel Discriminant Analysis With a Robust Kernel for Face Recognition and VerificationOAAI
OAAI分享收藏Effects of stereoisomers of estradiol on food intake, body weight and hoarding behavior in female rats雌二醇立体异构体对雌性大鼠摄食、体重和囤积行为的影响
PREAI
分享收藏No Relation Between Disease Activity Measured by Multiple Methods and REE in Childhood Crohn DiseaseOAAI
OAAI分享收藏分享收藏
查看更多内容