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.
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.