Face Segmentation For Identification Using Hidden Markov Models

Abstract

This paper details work done on face processing using a novel approach involving Hidden Markov Models. Experimental results from earlier work [14] 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.