An Improved NN Training Scheme Using Two-Stage LDA Features for Face Recognition

Seyed Bozorgtabar, Roland Goecke

Research output: A Conference proceeding or a Chapter in BookConference contribution

1 Citation (Scopus)

Abstract

This paper presents a new approach based on a Two-Stage Linear Discriminant Analysis (Two-Stage LDA) and Conjugate Gradient Algorithms (CGAs) for face recognition. A Two-Stage LDA technique is proposed that utilises the null space of the sample covariance matrix as well as using the range space of the between-class scatter matrix to extract discriminant information. Classic Back Propagation (BP) is a widely used Neural Network (NN) training algorithm in many detectors and classifiers. However, it is both too slow for many practical problems and its performance is not satisfactory in many application areas, including face recognition. To overcome these problems, four CGA algorithms (Fletcher-Reeves CGA, Polak-Ribiere CGA, Powell-Beale CGA, scaled CGA) have been proposed, the utility of which we investigate here in combination with Two-Stage LDA features. To further improve the accuracy, a modified AdaBoost.M1 approach was employed, which combines results of several NN classifiers as a single strong classifier. Experiments are performed on the ORL, FERET and AR face databases. The results show that all of the proposed methods lead to increased recognition rates and shorter training times compared to the classic BP.
Original languageEnglish
Title of host publicationNeural Information Processing - LNCS 7667
EditorsTingwen Huang, Zhigang Zeng, Chuangdong Li, Chi Sing Leung
Place of PublicationBerlin Heidelberg
PublisherSpringer
Pages662-671
Number of pages10
ISBN (Print)9783642344992
DOIs
Publication statusPublished - 2012
Event19th International Conference on Neural Information Processing 2012 - Doha, Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Conference

Conference19th International Conference on Neural Information Processing 2012
CountryQatar
CityDoha
Period12/11/1215/11/12

Fingerprint

Discriminant analysis
Face recognition
Neural networks
Classifiers
Backpropagation
Adaptive boosting
Covariance matrix
Detectors

Cite this

Bozorgtabar, S., & Goecke, R. (2012). An Improved NN Training Scheme Using Two-Stage LDA Features for Face Recognition. In T. Huang, Z. Zeng, C. Li, & C. S. Leung (Eds.), Neural Information Processing - LNCS 7667 (pp. 662-671). Berlin Heidelberg: Springer. https://doi.org/10.1007/978-3-642-34500-5_78
Bozorgtabar, Seyed ; Goecke, Roland. / An Improved NN Training Scheme Using Two-Stage LDA Features for Face Recognition. Neural Information Processing - LNCS 7667. editor / Tingwen Huang ; Zhigang Zeng ; Chuangdong Li ; Chi Sing Leung. Berlin Heidelberg : Springer, 2012. pp. 662-671
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abstract = "This paper presents a new approach based on a Two-Stage Linear Discriminant Analysis (Two-Stage LDA) and Conjugate Gradient Algorithms (CGAs) for face recognition. A Two-Stage LDA technique is proposed that utilises the null space of the sample covariance matrix as well as using the range space of the between-class scatter matrix to extract discriminant information. Classic Back Propagation (BP) is a widely used Neural Network (NN) training algorithm in many detectors and classifiers. However, it is both too slow for many practical problems and its performance is not satisfactory in many application areas, including face recognition. To overcome these problems, four CGA algorithms (Fletcher-Reeves CGA, Polak-Ribiere CGA, Powell-Beale CGA, scaled CGA) have been proposed, the utility of which we investigate here in combination with Two-Stage LDA features. To further improve the accuracy, a modified AdaBoost.M1 approach was employed, which combines results of several NN classifiers as a single strong classifier. Experiments are performed on the ORL, FERET and AR face databases. The results show that all of the proposed methods lead to increased recognition rates and shorter training times compared to the classic BP.",
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Bozorgtabar, S & Goecke, R 2012, An Improved NN Training Scheme Using Two-Stage LDA Features for Face Recognition. in T Huang, Z Zeng, C Li & CS Leung (eds), Neural Information Processing - LNCS 7667. Springer, Berlin Heidelberg, pp. 662-671, 19th International Conference on Neural Information Processing 2012, Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34500-5_78

An Improved NN Training Scheme Using Two-Stage LDA Features for Face Recognition. / Bozorgtabar, Seyed; Goecke, Roland.

Neural Information Processing - LNCS 7667. ed. / Tingwen Huang; Zhigang Zeng; Chuangdong Li; Chi Sing Leung. Berlin Heidelberg : Springer, 2012. p. 662-671.

Research output: A Conference proceeding or a Chapter in BookConference contribution

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N2 - This paper presents a new approach based on a Two-Stage Linear Discriminant Analysis (Two-Stage LDA) and Conjugate Gradient Algorithms (CGAs) for face recognition. A Two-Stage LDA technique is proposed that utilises the null space of the sample covariance matrix as well as using the range space of the between-class scatter matrix to extract discriminant information. Classic Back Propagation (BP) is a widely used Neural Network (NN) training algorithm in many detectors and classifiers. However, it is both too slow for many practical problems and its performance is not satisfactory in many application areas, including face recognition. To overcome these problems, four CGA algorithms (Fletcher-Reeves CGA, Polak-Ribiere CGA, Powell-Beale CGA, scaled CGA) have been proposed, the utility of which we investigate here in combination with Two-Stage LDA features. To further improve the accuracy, a modified AdaBoost.M1 approach was employed, which combines results of several NN classifiers as a single strong classifier. Experiments are performed on the ORL, FERET and AR face databases. The results show that all of the proposed methods lead to increased recognition rates and shorter training times compared to the classic BP.

AB - This paper presents a new approach based on a Two-Stage Linear Discriminant Analysis (Two-Stage LDA) and Conjugate Gradient Algorithms (CGAs) for face recognition. A Two-Stage LDA technique is proposed that utilises the null space of the sample covariance matrix as well as using the range space of the between-class scatter matrix to extract discriminant information. Classic Back Propagation (BP) is a widely used Neural Network (NN) training algorithm in many detectors and classifiers. However, it is both too slow for many practical problems and its performance is not satisfactory in many application areas, including face recognition. To overcome these problems, four CGA algorithms (Fletcher-Reeves CGA, Polak-Ribiere CGA, Powell-Beale CGA, scaled CGA) have been proposed, the utility of which we investigate here in combination with Two-Stage LDA features. To further improve the accuracy, a modified AdaBoost.M1 approach was employed, which combines results of several NN classifiers as a single strong classifier. Experiments are performed on the ORL, FERET and AR face databases. The results show that all of the proposed methods lead to increased recognition rates and shorter training times compared to the classic BP.

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Bozorgtabar S, Goecke R. An Improved NN Training Scheme Using Two-Stage LDA Features for Face Recognition. In Huang T, Zeng Z, Li C, Leung CS, editors, Neural Information Processing - LNCS 7667. Berlin Heidelberg: Springer. 2012. p. 662-671 https://doi.org/10.1007/978-3-642-34500-5_78