DocumentCode
1940687
Title
Notice of Retraction
Improving large-scale population recognition through structure optimization
Author
Sue Inn Ch´ng ; Kah-Phooi Seng ; Li-Minn Ang
Author_Institution
Dept. of Electr. & Electron. Eng., Univ. of Nottingham, Semenyih, Malaysia
Volume
5
fYear
2010
fDate
9-11 July 2010
Firstpage
380
Lastpage
383
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A problem that is commonly faced by large-scale population system is the high-dimensionality of data that needs to be processed at a given time. In this paper, a new face recognition training structure is proposed in which the large-scale population is split into smaller groups to be processed separately. To improve classification the proposed system uses global and local linear discriminant analysis together with a similarity measure to maximize the separation of features within each group. Implementations of the proposed structure indicate that the presented structure has a better performance and faster training time compared to a conventional training structure.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A problem that is commonly faced by large-scale population system is the high-dimensionality of data that needs to be processed at a given time. In this paper, a new face recognition training structure is proposed in which the large-scale population is split into smaller groups to be processed separately. To improve classification the proposed system uses global and local linear discriminant analysis together with a similarity measure to maximize the separation of features within each group. Implementations of the proposed structure indicate that the presented structure has a better performance and faster training time compared to a conventional training structure.
Keywords
face recognition; feature extraction; image classification; optimisation; face recognition; feature extraction; global discriminant analysis; image classification; large-scale population system; local linear discriminant analysis; structure optimization; Face; Indexes; Face recognition; large-scale population database; parallel neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5564102
Filename
5564102
Link To Document