Curvelet-based illumination invariant feature extraction for face recognition

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

Research output: Book chapter/Published conference paperConference paper

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 languageEnglish
Title of host publicationICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics
Pages458-462
Number of pages5
DOIs
Publication statusPublished - 01 Dec 2010
Event2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010 - Kuala Lumpur, Malaysia
Duration: 05 Dec 201007 Dec 2010

Conference

Conference2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010
CountryMalaysia
CityKuala Lumpur
Period05/12/1007/12/10

Fingerprint

Face recognition
Feature extraction
Lighting

Cite this

Ch'Ng, S. I., Seng, K. P., & Ang, L. M. (2010). Curvelet-based illumination invariant feature extraction for face recognition. In ICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics (pp. 458-462). [5735123] https://doi.org/10.1109/ICCAIE.2010.5735123
Ch'Ng, Sue Inn ; Seng, Kah Phooi ; Ang, Li Minn. / Curvelet-based illumination invariant feature extraction for face recognition. ICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics. 2010. pp. 458-462
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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.",
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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

Curvelet-based illumination invariant feature extraction for face recognition. / Ch'Ng, Sue Inn; Seng, Kah Phooi; Ang, Li Minn.

ICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics. 2010. p. 458-462 5735123.

Research output: Book chapter/Published conference paperConference paper

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

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AU - Seng, Kah Phooi

AU - Ang, Li Minn

PY - 2010/12/1

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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.

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KW - Multiband feature technique

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Ch'Ng SI, Seng KP, Ang LM. Curvelet-based illumination invariant feature extraction for face recognition. In ICCAIE 2010 - 2010 International Conference on Computer Applications and Industrial Electronics. 2010. p. 458-462. 5735123 https://doi.org/10.1109/ICCAIE.2010.5735123