Notice of Retraction: Improving large-scale population recognition through structure optimization

Sue Inn Ch'ng; Kah Phooi Seng; Li Minn Ang

doi:10.1109/ICCSIT.2010.5564102

Notice of Retraction: Improving large-scale population recognition through structure optimization

Sue Inn Ch'ng, Kah Phooi Seng, Li Minn Ang

Materials and Manufacturing Engineering

Research output: Chapter in Book or Report/Conference proceeding › Chapter › peer-review

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010
Publisher	IEEE Computer Society
Pages	380-383
Number of pages	4
ISBN (Print)	9781424455386
DOIs	https://doi.org/10.1109/ICCSIT.2010.5564102
Publication status	Published - 2010
Externally published	Yes

Publication series

Name	Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010
Volume	5

Keywords

Face recognition
Large-scale population database
Parallel neural networks

Access to Document

10.1109/ICCSIT.2010.5564102

Cite this

Ch'ng, S. I., Seng, K. P., & Ang, L. M. (2010). Notice of Retraction: Improving large-scale population recognition through structure optimization. In Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010 (pp. 380-383). Article 5564102 (Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010; Vol. 5). IEEE Computer Society. https://doi.org/10.1109/ICCSIT.2010.5564102

Ch'ng, Sue Inn ; Seng, Kah Phooi ; Ang, Li Minn. / Notice of Retraction : Improving large-scale population recognition through structure optimization. Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010. IEEE Computer Society, 2010. pp. 380-383 (Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010).

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title = "Notice of Retraction: Improving large-scale population recognition through structure optimization",

abstract = "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, Large-scale population database, Parallel neural networks",

author = "Ch'ng, {Sue Inn} and Seng, {Kah Phooi} and Ang, {Li Minn}",

year = "2010",

doi = "10.1109/ICCSIT.2010.5564102",

language = "English",

isbn = "9781424455386",

series = "Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010",

publisher = "IEEE Computer Society",

pages = "380--383",

booktitle = "Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010",

}

Ch'ng, SI, Seng, KP & Ang, LM 2010, Notice of Retraction: Improving large-scale population recognition through structure optimization. in Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010., 5564102, Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010, vol. 5, IEEE Computer Society, pp. 380-383. https://doi.org/10.1109/ICCSIT.2010.5564102

Notice of Retraction: Improving large-scale population recognition through structure optimization. / Ch'ng, Sue Inn; Seng, Kah Phooi; Ang, Li Minn.
Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010. IEEE Computer Society, 2010. p. 380-383 5564102 (Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010; Vol. 5).

Research output: Chapter in Book or Report/Conference proceeding › Chapter › peer-review

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T1 - Notice of Retraction

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AU - Ang, Li Minn

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N2 - 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.

AB - 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.

KW - Face recognition

KW - Large-scale population database

KW - Parallel neural networks

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DO - 10.1109/ICCSIT.2010.5564102

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SN - 9781424455386

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BT - Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010

PB - IEEE Computer Society

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Ch'ng SI, Seng KP, Ang LM. Notice of Retraction: Improving large-scale population recognition through structure optimization. In Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010. IEEE Computer Society. 2010. p. 380-383. 5564102. (Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010). doi: 10.1109/ICCSIT.2010.5564102

Notice of Retraction: Improving large-scale population recognition through structure optimization

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