A study on distance measures of tensor manifold for face recognition

Yeong Khang Lee, Andrew Beng Jin Teoh

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publication13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479939428
DOIs
Publication statusPublished - 2014 Sep 30
Event13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Kota Kinabalu, Malaysia
Duration: 2014 Jan 152014 Jan 18

Publication series

Name13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings

Other

Other13th International Conference on Electronics, Information, and Communication, ICEIC 2014
Country/TerritoryMalaysia
CityKota Kinabalu
Period14/1/1514/1/18

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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