Simultaneous and orthogonal decomposition of data using Multimodal Discriminant Analysis
- 1 September 2009
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We present Multimodal Discriminant Analysis (MMDA), a novel method for decomposing variations in a dataset into independent factors (modes). For face images, MMDA effectively separates personal identity, illumination and pose into orthogonal subspaces. MMDA is based on maximizing the Fisher Criterion on all modes at the same time, and is therefore well-suited for multimodal and mode-invariant pattern recognition. We also show that MMDA may be used for dimension reduction, and for synthesizing images under novel illumination and even novel personal identity.Keywords
This publication has 11 references indexed in Scilit:
- Multi-PIEPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Discriminant Subspace Analysis: A Fukunaga-Koontz ApproachIEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
- When Fisher meets Fukunaga-Koontz: A New Look at Linear DiscriminantsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Face transfer with multilinear modelsACM Transactions on Graphics, 2005
- Multilinear subspace analysis of image ensemblesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Simultaneous extraction of functional face subspacesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Learning bilinear models for two-factor problems in visionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Principal manifolds and probabilistic subspaces for visual recognitionIeee Transactions On Pattern Analysis and Machine Intelligence, 2002
- Eigenfaces vs. Fisherfaces: recognition using class specific linear projectionIeee Transactions On Pattern Analysis and Machine Intelligence, 1997
- Eigenfaces for RecognitionJournal of Cognitive Neuroscience, 1991