|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM316769754 |
| 003 |
DE-627 |
| 005 |
20231225161809.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231225s2022 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2020.3033994
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1055.xml
|
| 035 |
|
|
|a (DE-627)NLM316769754
|
| 035 |
|
|
|a (NLM)33108280
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Zhang, Chao
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Enhanced Group Sparse Regularized Nonconvex Regression for Face Recognition
|
| 264 |
|
1 |
|c 2022
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 05.04.2022
|
| 500 |
|
|
|a Date Revised 09.07.2022
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a Regression analysis based methods have shown strong robustness and achieved great success in face recognition. In these methods, convex l1-norm and nuclear norm are usually utilized to approximate the l0-norm and rank function. However, such convex relaxations may introduce a bias and lead to a suboptimal solution. In this paper, we propose a novel Enhanced Group Sparse regularized Nonconvex Regression (EGSNR) method for robust face recognition. An upper bounded nonconvex function is introduced to replace l1-norm for sparsity, which alleviates the bias problem and adverse effects caused by outliers. To capture the characteristics of complex errors, we propose a mixed model by combining γ-norm and matrix γ-norm induced from the nonconvex function. Furthermore, an l2,γ-norm based regularizer is designed to directly seek the interclass sparsity or group sparsity instead of traditional l2,1-norm. The locality of data, i.e., the distance between the query sample and multi-subspaces, is also taken into consideration. This enhanced group sparse regularizer enables EGSNR to learn more discriminative representation coefficients. Comprehensive experiments on several popular face datasets demonstrate that the proposed EGSNR outperforms the state-of-the-art regression based methods for robust face recognition
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 700 |
1 |
|
|a Li, Huaxiong
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Chen, Chunlin
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Qian, Yuhua
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhou, Xianzhong
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 5 vom: 27. Mai, Seite 2438-2452
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
|
| 773 |
1 |
8 |
|g volume:44
|g year:2022
|g number:5
|g day:27
|g month:05
|g pages:2438-2452
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2020.3033994
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 44
|j 2022
|e 5
|b 27
|c 05
|h 2438-2452
|