Face recognition using hidden Markov model supervectors

Hidden Markov Model (HMM);supervectors;Gaussian mixture models;Kullback-Leibler divergence

Soberal, Daniel ; Hasegawa-Johnson ; Mark A.

关键词: Hidden Markov Model (HMM);    supervectors;    Gaussian mixture models;    Kullback-Leibler divergence;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/72839/Daniel_Soberal.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

This project attempts to boost the results of face recognition algorithms already established to performface recognition by augmenting the architecture and using HMM-based supervector classification. In thisthesis, the work of Tang’s 2010 dissertation is used such that the HMM based classifier takes on aUBM-MAP adaptation based approach. In addition, Tang’s work is extended to the case of pseudo2-dimensional HMMs. Thus, a supervector classifier for pseudo 2DHMMs is developed and then applied tothe task of face recognition. When the recognition algorithm is applied to the ORL database, the resultsshow that the algorithm is able to either perform as well as other face recognition algorithms applied tothis database, or actually outperform them.