A study on distance measures of tensor manifold for face recognition

Yeong Khang Lee; Andrew Beng Jin Teoh

doi:10.1109/ELINFOCOM.2014.6914365

A study on distance measures of tensor manifold for face recognition

Yeong Khang Lee, Andrew Beng Jin Teoh

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Gabor-based region covariance matrices (GRCM) or known as tensor are a powerful face image descriptor and have shown promising result in face recognition. The GRCM lie on tensor manifold is inherently non-Euclidean. As such the distance measure on tensor manifold should take the geometry characteristic of the curvature into account. Presently, Affine Invariant Riemannian Metric is the most popular geodesic distance used in literature despite its heavy computation load. This paper studies several alternative distance measures and investigate their tradeoff between performance and computation time.

Original language	English
Title of host publication	13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479939428
DOIs	https://doi.org/10.1109/ELINFOCOM.2014.6914365
Publication status	Published - 2014 Sept 30
Event	13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Kota Kinabalu, Malaysia Duration: 2014 Jan 15 → 2014 Jan 18

Publication series

Name	13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings

Other

Other	13th International Conference on Electronics, Information, and Communication, ICEIC 2014
Country/Territory	Malaysia
City	Kota Kinabalu
Period	14/1/15 → 14/1/18

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/ELINFOCOM.2014.6914365

Cite this

Lee, Y. K., & Beng Jin Teoh, A. (2014). A study on distance measures of tensor manifold for face recognition. In 13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings Article 6914365 (13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ELINFOCOM.2014.6914365

Lee, Yeong Khang ; Beng Jin Teoh, Andrew. / A study on distance measures of tensor manifold for face recognition. 13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. (13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings).

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abstract = "Gabor-based region covariance matrices (GRCM) or known as tensor are a powerful face image descriptor and have shown promising result in face recognition. The GRCM lie on tensor manifold is inherently non-Euclidean. As such the distance measure on tensor manifold should take the geometry characteristic of the curvature into account. Presently, Affine Invariant Riemannian Metric is the most popular geodesic distance used in literature despite its heavy computation load. This paper studies several alternative distance measures and investigate their tradeoff between performance and computation time.",

author = "Lee, {Yeong Khang} and {Beng Jin Teoh}, Andrew",

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year = "2014",

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doi = "10.1109/ELINFOCOM.2014.6914365",

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Lee, YK & Beng Jin Teoh, A 2014, A study on distance measures of tensor manifold for face recognition. in 13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings., 6914365, 13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 13th International Conference on Electronics, Information, and Communication, ICEIC 2014, Kota Kinabalu, Malaysia, 14/1/15. https://doi.org/10.1109/ELINFOCOM.2014.6914365

A study on distance measures of tensor manifold for face recognition. / Lee, Yeong Khang; Beng Jin Teoh, Andrew.
13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. 6914365 (13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - Gabor-based region covariance matrices (GRCM) or known as tensor are a powerful face image descriptor and have shown promising result in face recognition. The GRCM lie on tensor manifold is inherently non-Euclidean. As such the distance measure on tensor manifold should take the geometry characteristic of the curvature into account. Presently, Affine Invariant Riemannian Metric is the most popular geodesic distance used in literature despite its heavy computation load. This paper studies several alternative distance measures and investigate their tradeoff between performance and computation time.

AB - Gabor-based region covariance matrices (GRCM) or known as tensor are a powerful face image descriptor and have shown promising result in face recognition. The GRCM lie on tensor manifold is inherently non-Euclidean. As such the distance measure on tensor manifold should take the geometry characteristic of the curvature into account. Presently, Affine Invariant Riemannian Metric is the most popular geodesic distance used in literature despite its heavy computation load. This paper studies several alternative distance measures and investigate their tradeoff between performance and computation time.

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Lee YK, Beng Jin Teoh A. A study on distance measures of tensor manifold for face recognition. In 13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. 6914365. (13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings). doi: 10.1109/ELINFOCOM.2014.6914365

A study on distance measures of tensor manifold for face recognition

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