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Title: Face recognition using Hidden Markov Models
Author: Samaria, Ferdinando Silvestro
ISNI:       0000 0001 3548 0292
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 1995
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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.
Supervisor: Not available Sponsor: Trinity College ; Olivetti Research Ltd
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: Face recognition ; Face segmentation ; automatic feature extraction ; Hidden Markov Models ; stochastic modelling