Curvelet-based illumination invariant feature extraction for face recognition

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

doi:10.1109/ICCAIE.2010.5735123

Curvelet-based illumination invariant feature extraction for face recognition

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

University of Nottingham

Research output: Book chapter/Published conference paper › Conference paper › peer-review

3 Citations (Scopus)

Abstract

This paper presents a curvelet-based illumination invariant feature extraction technique to solve the problem of varying illumination in face recognition. Multiband feature technique is employed to search the decomposed curvelet subbands for subbands which are insensitive to illumination variation. The two best performing subbands are then concatenated to form the Optimal Curvelet Subbands (OCS). To further improve the performance of OSC, histogram equalization is applied to enhance the contrast of the details. The proposed feature extraction method was evaluated on YaleB, EYaleB and AR database. The simulation results obtained shows that the proposed method outperforms its wavelet counterpart and that the extracted subbands are also applicable for other databases.

Original language	English
Title of host publication	ICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics
Pages	458-462
Number of pages	5
DOIs	https://doi.org/10.1109/ICCAIE.2010.5735123
Publication status	Published - 01 Dec 2010
Event	2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010 - Kuala Lumpur, Malaysia Duration: 05 Dec 2010 → 07 Dec 2010

Conference

Conference	2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010
Country/Territory	Malaysia
City	Kuala Lumpur
Period	05/12/10 → 07/12/10

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10.1109/ICCAIE.2010.5735123

Cite this

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title = "Curvelet-based illumination invariant feature extraction for face recognition",

abstract = "This paper presents a curvelet-based illumination invariant feature extraction technique to solve the problem of varying illumination in face recognition. Multiband feature technique is employed to search the decomposed curvelet subbands for subbands which are insensitive to illumination variation. The two best performing subbands are then concatenated to form the Optimal Curvelet Subbands (OCS). To further improve the performance of OSC, histogram equalization is applied to enhance the contrast of the details. The proposed feature extraction method was evaluated on YaleB, EYaleB and AR database. The simulation results obtained shows that the proposed method outperforms its wavelet counterpart and that the extracted subbands are also applicable for other databases.",

keywords = "Detail curvelet subbands, Fast discrete curvelet transform, Illumination invariant, Multiband feature technique",

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

year = "2010",

month = dec,

day = "1",

doi = "10.1109/ICCAIE.2010.5735123",

language = "English",

isbn = "9781424490554",

pages = "458--462",

booktitle = "ICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics",

note = "2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010 ; Conference date: 05-12-2010 Through 07-12-2010",

}

Ch'Ng, SI, Seng, KP & Ang, LM 2010, Curvelet-based illumination invariant feature extraction for face recognition. in ICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics., 5735123, pp. 458-462, 2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010, Kuala Lumpur, Malaysia, 05/12/10. https://doi.org/10.1109/ICCAIE.2010.5735123

TY - GEN

T1 - Curvelet-based illumination invariant feature extraction for face recognition

AU - Ch'Ng, Sue Inn

AU - Seng, Kah Phooi

AU - Ang, Li Minn

PY - 2010/12/1

Y1 - 2010/12/1

N2 - This paper presents a curvelet-based illumination invariant feature extraction technique to solve the problem of varying illumination in face recognition. Multiband feature technique is employed to search the decomposed curvelet subbands for subbands which are insensitive to illumination variation. The two best performing subbands are then concatenated to form the Optimal Curvelet Subbands (OCS). To further improve the performance of OSC, histogram equalization is applied to enhance the contrast of the details. The proposed feature extraction method was evaluated on YaleB, EYaleB and AR database. The simulation results obtained shows that the proposed method outperforms its wavelet counterpart and that the extracted subbands are also applicable for other databases.

AB - This paper presents a curvelet-based illumination invariant feature extraction technique to solve the problem of varying illumination in face recognition. Multiband feature technique is employed to search the decomposed curvelet subbands for subbands which are insensitive to illumination variation. The two best performing subbands are then concatenated to form the Optimal Curvelet Subbands (OCS). To further improve the performance of OSC, histogram equalization is applied to enhance the contrast of the details. The proposed feature extraction method was evaluated on YaleB, EYaleB and AR database. The simulation results obtained shows that the proposed method outperforms its wavelet counterpart and that the extracted subbands are also applicable for other databases.

KW - Detail curvelet subbands

KW - Fast discrete curvelet transform

KW - Illumination invariant

KW - Multiband feature technique

UR - https://www.scopus.com/pages/publications/79953865304

UR - https://www.scopus.com/pages/publications/79953865304#tab=citedBy

U2 - 10.1109/ICCAIE.2010.5735123

DO - 10.1109/ICCAIE.2010.5735123

M3 - Conference paper

AN - SCOPUS:79953865304

SN - 9781424490554

SP - 458

EP - 462

BT - ICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics

T2 - 2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010

Y2 - 5 December 2010 through 7 December 2010

ER -

Curvelet-based illumination invariant feature extraction for face recognition

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