tailieunhanh - Báo cáo hóa học: " Research Article Localized versus Locality-Preserving Subspace Projections for Face Recognition"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Localized versus Locality-Preserving Subspace Projections for Face Recognition | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2007 Article ID 17173 8 pages doi 2007 17173 Research Article Localized versus Locality-Preserving Subspace Projections for Face Recognition Iulian B. Ciocoiu1 and Hariton N. Costin2 3 1 Faculty of Electronics and Telecommunications Gh. Asachi Technical University of Iasi 700506 Iasi Romania 2 Faculty of Medical Bioengineering Gr. T Popa University of Medicine and Pharmacy 700115 Iasi Romania 3 Institute for Theoretical Computer Science Romanian Academy Iasi Branch 700506 Iasi Romania Received 1 May 2006 Revised 10 September 2006 Accepted 26 March 2007 Recommended by Tim Cootes Three different localized representation methods and a manifold learning approach to face recognition are compared in terms of recognition accuracy. The techniques under investigation are a local nonnegative matrix factorization LNMF b independent component analysis ICA c NMF with sparse constraints NMFsc d locality-preserving projections Laplacian faces . A systematic comparative analysis is conducted in terms of distance metric used number of selected features and sources of variability on AR and Olivetti face databases. Results indicate that the relative ranking of the methods is highly task-dependent and the performances vary significantly upon the distance metric used. Copyright 2007 I. B. Ciocoiu and H. N. Costin. 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. 1. INTRODUCTION Face recognition has represented for more than one decade one of the most active research areas in pattern recognition. A plethora of approaches has been proposed and evaluation standards have been defined but current solutions still need to be improved in order to cope with the recognition rates and robustness requirements of commercial products. A number of .

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