A methodology for evaluating robustness of face recognition algorithms with respect to variations in pose angle and illumination angle

Greg Little, Sreekar Krishna, John Black, Sethuraman Panchanathan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

86 Scopus citations

Abstract

In this paper, we present a methodology for precisely comparing the robustness of face recognition algorithms with respect to changes in pose angle and illumination angle. For this study, we have chosen four widely-used algorithms: two subspace analysis methods (Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA)) and two probabilistic learning methods (Hidden Markov Models (HMM) and Bayesian Intra-personal Classifier (BIC)). We compare the recognition robustness of these algorithms using a novel database (FacePix) that captures face images with a wide range of pose angles and illumination angles. We propose a method for deriving a robustness measure for each of these algorithms, with respect to pose and illumination angle changes. The results of this comparison indicate that the subspace methods perform more robustly than the probabilistic learning methods in the presence of pose and illumination angle changes.

Original languageEnglish (US)
Title of host publication2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing
PagesII89-II92
DOIs
StatePublished - Dec 1 2005
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: Mar 18 2005Mar 23 2005

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
VolumeII
ISSN (Print)1520-6149

Other

Other2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
CountryUnited States
CityPhiladelphia, PA
Period3/18/053/23/05

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Little, G., Krishna, S., Black, J., & Panchanathan, S. (2005). A methodology for evaluating robustness of face recognition algorithms with respect to variations in pose angle and illumination angle. In 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing (pp. II89-II92). [1415348] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. II). https://doi.org/10.1109/ICASSP.2005.1415348