Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry

Konstantinos Papachristou; Anastasios Tefas; Ioannis Pitas

doi:10.1109/ICIP.2015.7351391

Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry

Konstantinos Papachristou, Anastasios Tefas, Ioannis Pitas

Department of Electrical & Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

2 Citations (Scopus)

327 Downloads (Pure)

Abstract

In this paper a novel subspace learning technique is introduced for facial image analysis. The proposed technique takes into account the symmetry nature of facial images. This information is exploited by properly incorporating a symmetry constraint into the objective function of the Two-Dimensional Linear Discriminant Analysis (2DLDA) to determine symmetric projection vectors. The performance of the proposed Symmetric Two-Dimensional Linear Discriminant Analysis was evaluated on real face recognition databases. Experimental results highlight the superiority of the proposed technique in comparison to standard approach.

Original language	English
Title of host publication	2015 IEEE International Conference on Image Processing (ICIP 2015)
Subtitle of host publication	Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	3185-3189
Number of pages	5
ISBN (Electronic)	9781479983391
ISBN (Print)	9781479983407
DOIs	https://doi.org/10.1109/ICIP.2015.7351391
Publication status	Published - Jan 2016
Event	2015 IEEE International Conference on Image Processing (ICIP) - Quebec City, ON, Canada Duration: 27 Sept 2015 → 30 Sept 2015

Conference

Conference	2015 IEEE International Conference on Image Processing (ICIP)
Country/Territory	Canada
City	Quebec City, ON
Period	27/09/15 → 30/09/15

Keywords

facial image analysis
subspace learning
symmetry constraint
two-dimensional linear discriminant analysis

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10.1109/ICIP.2015.7351391

Ioannis Pitas - Facial image analysis based on two-dimentional linear discriminant analysis exploiting symmetry - 2015Accepted author manuscript, 321 KB

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Papachristou, K., Tefas, A., & Pitas, I. (2016). Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry. In 2015 IEEE International Conference on Image Processing (ICIP 2015): Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada (pp. 3185-3189). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICIP.2015.7351391

Papachristou, Konstantinos ; Tefas, Anastasios ; Pitas, Ioannis. / Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry. 2015 IEEE International Conference on Image Processing (ICIP 2015): Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada. Institute of Electrical and Electronics Engineers (IEEE), 2016. pp. 3185-3189

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title = "Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry",

abstract = "In this paper a novel subspace learning technique is introduced for facial image analysis. The proposed technique takes into account the symmetry nature of facial images. This information is exploited by properly incorporating a symmetry constraint into the objective function of the Two-Dimensional Linear Discriminant Analysis (2DLDA) to determine symmetric projection vectors. The performance of the proposed Symmetric Two-Dimensional Linear Discriminant Analysis was evaluated on real face recognition databases. Experimental results highlight the superiority of the proposed technique in comparison to standard approach.",

keywords = "facial image analysis, subspace learning, symmetry constraint, two-dimensional linear discriminant analysis",

author = "Konstantinos Papachristou and Anastasios Tefas and Ioannis Pitas",

year = "2016",

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doi = "10.1109/ICIP.2015.7351391",

language = "English",

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pages = "3185--3189",

booktitle = "2015 IEEE International Conference on Image Processing (ICIP 2015)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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note = "2015 IEEE International Conference on Image Processing (ICIP) ; Conference date: 27-09-2015 Through 30-09-2015",

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Papachristou, K, Tefas, A & Pitas, I 2016, Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry. in 2015 IEEE International Conference on Image Processing (ICIP 2015): Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada. Institute of Electrical and Electronics Engineers (IEEE), pp. 3185-3189, 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, ON, Canada, 27/09/15. https://doi.org/10.1109/ICIP.2015.7351391

Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry. / Papachristou, Konstantinos; Tefas, Anastasios; Pitas, Ioannis.
2015 IEEE International Conference on Image Processing (ICIP 2015): Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada. Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 3185-3189.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry

AU - Papachristou, Konstantinos

AU - Tefas, Anastasios

AU - Pitas, Ioannis

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N2 - In this paper a novel subspace learning technique is introduced for facial image analysis. The proposed technique takes into account the symmetry nature of facial images. This information is exploited by properly incorporating a symmetry constraint into the objective function of the Two-Dimensional Linear Discriminant Analysis (2DLDA) to determine symmetric projection vectors. The performance of the proposed Symmetric Two-Dimensional Linear Discriminant Analysis was evaluated on real face recognition databases. Experimental results highlight the superiority of the proposed technique in comparison to standard approach.

AB - In this paper a novel subspace learning technique is introduced for facial image analysis. The proposed technique takes into account the symmetry nature of facial images. This information is exploited by properly incorporating a symmetry constraint into the objective function of the Two-Dimensional Linear Discriminant Analysis (2DLDA) to determine symmetric projection vectors. The performance of the proposed Symmetric Two-Dimensional Linear Discriminant Analysis was evaluated on real face recognition databases. Experimental results highlight the superiority of the proposed technique in comparison to standard approach.

KW - facial image analysis

KW - subspace learning

KW - symmetry constraint

KW - two-dimensional linear discriminant analysis

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DO - 10.1109/ICIP.2015.7351391

M3 - Conference Contribution (Conference Proceeding)

SN - 9781479983407

SP - 3185

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BT - 2015 IEEE International Conference on Image Processing (ICIP 2015)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2015 IEEE International Conference on Image Processing (ICIP)

Y2 - 27 September 2015 through 30 September 2015

ER -

Papachristou K, Tefas A, Pitas I. Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry. In 2015 IEEE International Conference on Image Processing (ICIP 2015): Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada. Institute of Electrical and Electronics Engineers (IEEE). 2016. p. 3185-3189 Epub 2015 Sept 27. doi: 10.1109/ICIP.2015.7351391

Facial image analysis based on two-dimensional linear discriminant analysis exploiting symmetry

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