TY - GEN
T1 - A methodology for evaluating robustness of face recognition algorithms with respect to variations in pose angle and illumination angle
AU - Little, Greg
AU - Krishna, Sreekar
AU - Black, John
AU - Panchanathan, Sethuraman
PY - 2005/12/1
Y1 - 2005/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33646801648&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646801648&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2005.1415348
DO - 10.1109/ICASSP.2005.1415348
M3 - Conference contribution
AN - SCOPUS:33646801648
SN - 0780388747
SN - 9780780388741
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - II89-II92
BT - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing
T2 - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Y2 - 18 March 2005 through 23 March 2005
ER -