.png){kind=icon}
返回
Joint multi-subspace feature learning with singular value decomposition for robust single-sample face recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2024.109085.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Single -sample face recognition remains a significant challenge due to the difficulty in extracting discriminative features using only one facial image per individual in practical applications. In light of this, the paper introduces a new method for learning multiple subspace features using singular value decomposition (SVD). Specifically, we divide each facial image into two symmetrical halves to increase intra-class diversity. The SVD is subsequently applied to each half to create distinct geometric view subspaces. Then, discriminative features are learnt through performing 2 -dimension linear discriminant analysis (2DLDA) in each subspace. Lastly, the identification of faces is accomplished by using a k -nearest neighbour classifier (kNN) within each subspace followed by a majority voting strategy. The method proposed is extensively verified through rigorous experiments carried out on different databases such as CUHK, Extended Yale B, FRGCv2 and AR. The experimental outcomes unambiguously show that our approach consistently achieves competitive performance when compared to other methods.
Keyword:
Single-sample face recognition
Singular value decomposition
Multiple subspace features
Facial base image
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
引用论文
Learning features from covariance matrix of gabor wavelet for face recognition under adverse conditions.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Dominant singular value decomposition representation for face recognitionSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏分享收藏分享收藏
查看更多内容