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oai:www.ideals.illinois.edu:2142/81151 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Ma, Yi Wright, John N. 2015-09-25T20:09:47Z 2015-09-25T20:09:47Z 10000-01-01 2009 2009 Finally, we show how these theoretical developments lead to simple, scalable, and robust algorithms for face recognition in the presence of varying illumination and occlusion. The idea is extremely simple: seek the sparsest representation of the test image as a linear combination of training images plus a sparse error term due to occlusion. In addition to achieving excellent performance on public databases, this approach sheds light on several important issues in face recognition, such as the choice of features and robustness to corruption and occlusion. Made available in DSpace on 2015-09-25T20:09:47Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3395542.pdf: 7846761 bytes, checksum: ae572fab8e35314ba5d87880ded4ed94 (MD5) Previous issue date: 2009 Embargo set by: Seth Robbins for item 82432 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 199 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. http://hdl.handle.net/2142/81151 (MiAaPQ)AAI3395542 eng Engineering, Electronics and Electrical Error Correction for High-Dimensional Data via Convex Programming text Electrical and Computer Engineering Electrical and Computer Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.