TAILIEUCHUNG - Báo cáo sinh học: " Research Article A Multifactor Extension of Linear Discriminant Analysis for Face Recognition under Varying Pose and Illumination"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article A Multifactor Extension of Linear Discriminant Analysis for Face Recognition under Varying Pose and Illumination | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 158395 11 pages doi 2010 158395 Research Article A Multifactor Extension of Linear Discriminant Analysis for Face Recognition under Varying Pose and Illumination Sung Won Park and Marios Savvides Electrical and Computer Engineering Department Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 USA Correspondence should be addressed to Sung Won Park sungwonp@ Received 11 December 2009 Revised 27 April 2010 Accepted 20 May 2010 Academic Editor Robert W. Ives Copyright 2010 S. W. Park and M. Savvides. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 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. 1. .

• Sinh học
• Báo cáo sinh học hay
• cách trình bày báo cáo
• báo cáo sinh học
• công trình nghiên cứu sinh học
• tài liệu về sinh học