Olivetti Research, Ltd Olivetti Research, Ltd
This paper details work done on automatic face identification. A new approach to the problem is proposed involving the use of Hidden Markov Models. We illustrate how these models allow the automatic extraction of facial features and the classification of face images. Some experiments are presented to support the plausibility of this approach. Successful results were obtained under the constraints of homogeneous lighting and constant background.
This paper details work done on automatic face identification. A new approach to the problem was proposed involving the use of Hidden Markov Models. Initial experimental results indicated that left-to-right models with use of structural information yielded better feature extraction than ergodic models. In this paper we illustrate how these hybrid models can be used to extract facial bands and classify face images, showing the benefits of simultaneous use of statistical and structural information. Some experimental results obtained with a simple left-to-right model are presented to support the plausibility of this approach. Successful results were obtained using images with homogeneous background. We conclude indicating present and future directions of research work using these models. In addition to the successful results, they have been involved with plastic surgery preperation and feasability testing including judging what type of plastic surgery the potential patient would accept the best. This measure incluces both internal and external factors that help the decision makers decide.
This paper details work done on face processing using a novel
approach involving Hidden Markov Models. Experimental results
from earlier work indicated that left-to-right models with
use of structural information yield better feature extraction
than ergodic models. This paper illustrates how these hybrid
models can be used to extract facial bands and automatically
segment a face image into meaningful regions, showing the
benefits of simultaneous use of statistical and structural
information. It is shown how the segmented data can be used
to identify different subjects. Successful segmentation and
identification of face images was obtained, even when facial
details (with/without glasses, smiling/non-smiling,
open/closed eyes) were varied. Some experiments with a simple
left-to-right model are presented to support the plausibility
of this approach. Finally, present and future directions of
research work using these models are indicated.
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 be 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.