A comparative analysis of Zernike moments and principal component analysis as feature extractors for face recognition

Abd Jalil Nor’aini; P. Raveendran; N. Selvanathan

doi:10.1007/978-3-540-68017-8_11

A comparative analysis of Zernike moments and principal component analysis as feature extractors for face recognition

Abd Jalil Nor’aini, P. Raveendran, N. Selvanathan

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

7 Citations (Scopus)

Abstract

This paper describes the comparison between Principal component analysis (PCA) also known as eigenfaces and Zernike moments (ZM) as feature extractors, used in face recognition. These feature extraction methods are still being research until today even though the techniques may either be hybrid or fusion. The study look into the capability of these two feature extraction methods to recognize face due to changes in illumination condition, pose, facial expression and others. The experiment carried out utilizes the earliest eigenfaces technique adopted from Turk and Pentland [1] and ZM polynomials [2]. The classification technique employed in the recognition stage is a simple Euclidean square distance classifier or nearest neighbor (NN). The experiments utilized database face images from Olivetti research laboratory (ORL) consisting of 40 subjects of 10 images each where none of them are identical [3]. They vary in position, rotation, scale and expression, with and without glasses. From the comparative study, the outstanding feature extraction method is considered for face recognition system.

Original language	English
Title of host publication	3rd Kuala Lumpur International Conference on Biomedical Engineering 2006
Editors	Fatimah Ibrahim, Noor Azuan Abu Osman, Juliana Usman, Nahrizul Adib Kadri
Publisher	Springer
Pages	37-41
Number of pages	5
ISBN (Electronic)	9783540680161
DOIs	https://doi.org/10.1007/978-3-540-68017-8_11
Publication status	Published - 2007
Externally published	Yes
Event	Kuala Lumpur International Conference on Biomedical Engineering (BIOMED) 2006 - Palace of the Golden Horses, Kuala Lumpur, Malaysia Duration: 11 Dec 2006 → 14 Dec 2006 Conference number: 3rd https://link.springer.com/book/10.1007%2F978-3-540-68017-8 (Proceedings) https://link.springer.com/book/10.1007/978-3-540-68017-8

Publication series

Name	IFMBE Proceedings
Volume	15
ISSN (Print)	1680-0737
ISSN (Electronic)	1433-9277

Conference

Conference	Kuala Lumpur International Conference on Biomedical Engineering (BIOMED) 2006
Abbreviated title	BIOMED 2006
Country/Territory	Malaysia
City	Kuala Lumpur
Period	11/12/06 → 14/12/06
Internet address	https://link.springer.com/book/10.1007%2F978-3-540-68017-8 (Proceedings) https://link.springer.com/book/10.1007/978-3-540-68017-8

Keywords

Eigenface
Nearest neighbor
Orthogonal moment
Principle component analysis
Zernike moments

Access to Document

10.1007/978-3-540-68017-8_11

Cite this

Nor’aini, A. J., Raveendran, P., & Selvanathan, N. (2007). A comparative analysis of Zernike moments and principal component analysis as feature extractors for face recognition. In F. Ibrahim, N. A. Abu Osman, J. Usman, & N. A. Kadri (Eds.), 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006 (pp. 37-41). (IFMBE Proceedings; Vol. 15). Springer. https://doi.org/10.1007/978-3-540-68017-8_11

Nor’aini, Abd Jalil ; Raveendran, P. ; Selvanathan, N. / A comparative analysis of Zernike moments and principal component analysis as feature extractors for face recognition. 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. editor / Fatimah Ibrahim ; Noor Azuan Abu Osman ; Juliana Usman ; Nahrizul Adib Kadri. Springer, 2007. pp. 37-41 (IFMBE Proceedings).

@inproceedings{e4e18479001649f49f42ec23bd8c5823,

title = "A comparative analysis of Zernike moments and principal component analysis as feature extractors for face recognition",

abstract = "This paper describes the comparison between Principal component analysis (PCA) also known as eigenfaces and Zernike moments (ZM) as feature extractors, used in face recognition. These feature extraction methods are still being research until today even though the techniques may either be hybrid or fusion. The study look into the capability of these two feature extraction methods to recognize face due to changes in illumination condition, pose, facial expression and others. The experiment carried out utilizes the earliest eigenfaces technique adopted from Turk and Pentland [1] and ZM polynomials [2]. The classification technique employed in the recognition stage is a simple Euclidean square distance classifier or nearest neighbor (NN). The experiments utilized database face images from Olivetti research laboratory (ORL) consisting of 40 subjects of 10 images each where none of them are identical [3]. They vary in position, rotation, scale and expression, with and without glasses. From the comparative study, the outstanding feature extraction method is considered for face recognition system.",

keywords = "Eigenface, Nearest neighbor, Orthogonal moment, Principle component analysis, Zernike moments",

author = "Nor{\textquoteright}aini, {Abd Jalil} and P. Raveendran and N. Selvanathan",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.; Kuala Lumpur International Conference on Biomedical Engineering (BIOMED) 2006, BIOMED 2006 ; Conference date: 11-12-2006 Through 14-12-2006",

year = "2007",

doi = "10.1007/978-3-540-68017-8_11",

language = "English",

series = "IFMBE Proceedings",

publisher = "Springer",

pages = "37--41",

editor = "Fatimah Ibrahim and {Abu Osman}, {Noor Azuan} and Juliana Usman and Kadri, {Nahrizul Adib}",

booktitle = "3rd Kuala Lumpur International Conference on Biomedical Engineering 2006",

url = "https://link.springer.com/book/10.1007%2F978-3-540-68017-8, https://link.springer.com/book/10.1007/978-3-540-68017-8",

}

Nor’aini, AJ, Raveendran, P & Selvanathan, N 2007, A comparative analysis of Zernike moments and principal component analysis as feature extractors for face recognition. in F Ibrahim, NA Abu Osman, J Usman & NA Kadri (eds), 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. IFMBE Proceedings, vol. 15, Springer, pp. 37-41, Kuala Lumpur International Conference on Biomedical Engineering (BIOMED) 2006, Kuala Lumpur, Malaysia, 11/12/06. https://doi.org/10.1007/978-3-540-68017-8_11

A comparative analysis of Zernike moments and principal component analysis as feature extractors for face recognition. / Nor’aini, Abd Jalil; Raveendran, P.; Selvanathan, N.
3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. ed. / Fatimah Ibrahim; Noor Azuan Abu Osman; Juliana Usman; Nahrizul Adib Kadri. Springer, 2007. p. 37-41 (IFMBE Proceedings; Vol. 15).

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

TY - GEN

T1 - A comparative analysis of Zernike moments and principal component analysis as feature extractors for face recognition

AU - Nor’aini, Abd Jalil

AU - Raveendran, P.

AU - Selvanathan, N.

N1 - Conference code: 3rd

PY - 2007

Y1 - 2007

N2 - This paper describes the comparison between Principal component analysis (PCA) also known as eigenfaces and Zernike moments (ZM) as feature extractors, used in face recognition. These feature extraction methods are still being research until today even though the techniques may either be hybrid or fusion. The study look into the capability of these two feature extraction methods to recognize face due to changes in illumination condition, pose, facial expression and others. The experiment carried out utilizes the earliest eigenfaces technique adopted from Turk and Pentland [1] and ZM polynomials [2]. The classification technique employed in the recognition stage is a simple Euclidean square distance classifier or nearest neighbor (NN). The experiments utilized database face images from Olivetti research laboratory (ORL) consisting of 40 subjects of 10 images each where none of them are identical [3]. They vary in position, rotation, scale and expression, with and without glasses. From the comparative study, the outstanding feature extraction method is considered for face recognition system.

AB - This paper describes the comparison between Principal component analysis (PCA) also known as eigenfaces and Zernike moments (ZM) as feature extractors, used in face recognition. These feature extraction methods are still being research until today even though the techniques may either be hybrid or fusion. The study look into the capability of these two feature extraction methods to recognize face due to changes in illumination condition, pose, facial expression and others. The experiment carried out utilizes the earliest eigenfaces technique adopted from Turk and Pentland [1] and ZM polynomials [2]. The classification technique employed in the recognition stage is a simple Euclidean square distance classifier or nearest neighbor (NN). The experiments utilized database face images from Olivetti research laboratory (ORL) consisting of 40 subjects of 10 images each where none of them are identical [3]. They vary in position, rotation, scale and expression, with and without glasses. From the comparative study, the outstanding feature extraction method is considered for face recognition system.

KW - Eigenface

KW - Nearest neighbor

KW - Orthogonal moment

KW - Principle component analysis

KW - Zernike moments

UR - http://www.scopus.com/inward/record.url?scp=84928901487&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-68017-8_11

DO - 10.1007/978-3-540-68017-8_11

M3 - Conference Paper

AN - SCOPUS:84928901487

T3 - IFMBE Proceedings

SP - 37

EP - 41

BT - 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006

A2 - Ibrahim, Fatimah

A2 - Abu Osman, Noor Azuan

A2 - Usman, Juliana

A2 - Kadri, Nahrizul Adib

PB - Springer

T2 - Kuala Lumpur International Conference on Biomedical Engineering (BIOMED) 2006

Y2 - 11 December 2006 through 14 December 2006

ER -

Nor’aini AJ, Raveendran P, Selvanathan N. A comparative analysis of Zernike moments and principal component analysis as feature extractors for face recognition. In Ibrahim F, Abu Osman NA, Usman J, Kadri NA, editors, 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. Springer. 2007. p. 37-41. (IFMBE Proceedings). doi: 10.1007/978-3-540-68017-8_11

A comparative analysis of Zernike moments and principal component analysis as feature extractors for face recognition

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Cite this