Parameterisation of a stochastic model for human faceidentification
Samaria, F.S.
Harter, A.C.
Dept. of Eng., Cambridge Univ.;
This paper appears in: Applications of Computer Vision, 1994., Proceedings of the Second IEEE Workshop on
Publication Date: 5-7 Dec 1994
On page(s): 138-142
Meeting Date: 12/05/1994 - 12/07/1994
Location: Sarasota, FL, USA
ISBN: 0-8186-6410-X
References Cited: 10
INSPEC Accession Number: 4852572
Digital Object Identifier: 10.1109/ACV.1994.341300
Current Version Published: 2002-08-06
Abstract
Recent work on face identification using continuous density Hidden
Markov Models (HMMs) has shown that stochastic modelling can be used
successfully to encode feature information. When frontal images of faces
are sampled using top-bottom scanning, there is a natural order in which
the features appear and this can be conveniently modelled using a
top-bottom HMM. However, a top-bottom HMM is characterised by different
parameters, the choice of which has so far been based on subjective
intuition. This paper presents a set of experimental results in which
various HMM parameterisations are analysed
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
