Face recognition using Hidden Markov Models
Samaria, Ferdinando Silvestro
University of Cambridge
Department of Engineering
Doctor of Philosophy (PhD)
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Samaria, F. S. (1995). Face recognition using Hidden Markov Models (Doctoral thesis). https://doi.org/10.17863/CAM.14051
This dissertation introduces work on face recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowledge with statistical information, HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, with 5 training images and 5 test images for each. It is shown how standard one-dimensional HMMs in the shape of top-bottom models can be parameterised, yielding successful recognition rates of up to around 85%. The insights gained from top-bottom models are extended to pseudo two-dimensional HMMs, which offer a better and more flexible model, that describes some of the twodimensional dependencies missed by the standard one-dimensional model. It is shown how pseudo two-dimensional HMMs can be implemented, yielding successful recognition rates of up to around 95%. The performance of the HMMs is compared with the Eigenface approach and various domain and resolution experiments are also carried out. Finally, the performance of the HMM is evaluated in a fully automated system, where database images are cropped automatically.
Face recognition, Face segmentation, automatic feature extraction, Hidden Markov Models, stochastic modelling
This record's DOI: https://doi.org/10.17863/CAM.14051
All Rights Reserved, This work was supported by a Trinity College Internal Graduate Studentship and an Olivetti Research Ltd, CASE award.
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