Face recognition using probabilistic two-dimensional principal component analysis and its mixture model

Authors:
Haixian Wang;Zilan Hu
Affiliations:
Research Center for Learning Science, Southeast University, Nanjing, Jiangsu, P.R. China;School of Mathematics and Physics, Anhui University of Technology, Maanshan, Anhui, P.R. China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

Citing 5
Cited 0

Mixtures of probabilistic principal component analyzers

Neural Computation
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
The equivalence of two-dimensional PCA to line-based PCA

Pattern Recognition Letters
2005 Special Issue: Generalized 2D principal component analysis for face image representation and recognition

Neural Networks - 2005 Special issue: IJCNN 2005
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, by supposing a parametric Gaussian distribution over the image space (spanned by the row vectors of 2D image matrices) and a spherical Gaussian noise model for the image, we endow the two-dimensional principal component analysis (2DPCA) with a probabilistic framework called probabilistic 2DPCA (P2DPCA), which is robust to noise. Further, by using the probabilistic perspective of P2DPCA, we extend P2DPCA to a mixture of local P2DPCA models (MP2DPCA). MP2DPCA offers us a method of being able to model faces in unconstrained (complex) environment with possibly large variation. The model parameters could be fitted on the basis of maximum likelihood (ML) estimation via the expectation maximization (EM) algorithm. The experimental recognition results on UMIST face database confirm the effectivity of the proposed methods.