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Title:Face recognition using hidden Markov model supervectors
Author(s):Soberal, Daniel
Advisor(s):Hasegawa-Johnson, Mark A.
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Hidden Markov Model (HMM)
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.
Issue Date:2015-01-21
Rights Information:Copyright 2014 Daniel Soberal
Date Available in IDEALS:2015-01-21
Date Deposited:2014-12

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