A Multifactor Extension of Linear Discriminant Analysis for Face Recognition under Varying Pose and Illumination

作者：Park Sung Won^*; Savvides Marios

来源：Eurasip Journal on Advances in Signal Processing, 2010, 158395.

DOI：10.1155/2010/158395

Linear Discriminant Analysis (LDA) and Multilinear Principal Component Analysis ( MPCA) are leading subspace methods for achieving dimension reduction based on supervised learning. Both LDA and MPCA use class labels of data samples to calculate subspaces onto which these samples are projected. Furthermore, both methods have been successfully applied to face recognition. Although LDA and MPCA share common goals and methodologies, in previous research they have been applied separately and independently. In this paper, we propose an extension of LDA to multiple factor frameworks. Our proposed method, Multifactor Discriminant Analysis, aims to obtain multilinear projections that maximize the between-class scatter while minimizing the withinclass scatter, which is the same core fundamental objective of LDA. Moreover, Multifactor Discriminant Analysis (MDA), like MPCA, uses multifactor analysis and calculates subject parameters that represent the characteristics of subjects and are invariant to other changes, such as viewpoints or lighting conditions. In this way, our proposed MDA combines the best virtues of both LDA and MPCA for face recognition.

• 出版日期2010