Face recognition using hidden Markov model supervectors
Soberal, Daniel
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https://hdl.handle.net/2142/72839
Description
Title
Face recognition using hidden Markov model supervectors
Author(s)
Soberal, Daniel
Issue Date
2015-01-21
Director of Research (if dissertation) or Advisor (if thesis)
Hasegawa-Johnson, Mark A.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Hidden Markov Model (HMM)
supervectors
Gaussian mixture models
Kullback-Leibler divergence
Abstract
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.
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