2026-09-22T10:39:21Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/72839 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Hasegawa-Johnson, Mark A. Soberal, Daniel 2015-01-21T19:48:45Z 2015-01-21T19:48:45Z 2014-12 2015-01-21 2014-12 This project attempts to boost the results of face recognition algorithms already established to perform face recognition by augmenting the architecture and using HMM-based supervector classification. In this thesis, the work of Tang’s 2010 dissertation is used such that the HMM based classifier takes on a UBM-MAP adaptation based approach. In addition, Tang’s work is extended to the case of pseudo 2-dimensional HMMs. Thus, a supervector classifier for pseudo 2DHMMs is developed and then applied to the task of face recognition. When the recognition algorithm is applied to the ORL database, the results show that the algorithm is able to either perform as well as other face recognition algorithms applied to this database, or actually outperform them. Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2014-12-11T13:48:23Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Soberal_Daniel.pdf: 1102521 bytes, checksum: 2ba35ed17537eed0c884be0236979a01 (MD5) Made available in DSpace on 2015-01-21T19:48:45Z (GMT). No. of bitstreams: 1 Daniel_Soberal.pdf: 1102604 bytes, checksum: dafaf394fdcd413d84bdded598234da9 (MD5) http://hdl.handle.net/2142/72839 en Copyright 2014 Daniel Soberal Hidden Markov Model (HMM) supervectors Gaussian mixture models Kullback-Leibler divergence Face recognition using hidden Markov model supervectors text Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Thesis M.S. MS:Electr & Computer Eng-UIUC 10KS1200MS