A Statistical Multiresolution Approach for Face Recognition Using Structural Hidden Markov Models

P Nicholl,D Bouchaffra,A Amira,R H Perrott

doi:10.1155/2008/675787

Abstract

This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy.

Highlights

With the current perceived world security situation, governments, as well as businesses, require reliable methods to accurately identify individuals, without overly infringing on rights to privacy or requiring significant compliance on the part of the individual being recognized
The objective of the work presented in this paper is to develop a hybrid approach for face identification using structural hidden Markov model (SHMM) for the first time
We have carried out an analysis of the benefits of using discrete wavelet transform (DWT) along with Hidden Markov models (HMMs) for face recognition

Summary

INTRODUCTION

With the current perceived world security situation, governments, as well as businesses, require reliable methods to accurately identify individuals, without overly infringing on rights to privacy or requiring significant compliance on the part of the individual being recognized. Samaria and Young used image pixel values to build a top-down model of a face using HMMs. Nefian and Hayes [12] modified the approach by using discrete cosine transform (DCT) coefficients to form observation vectors. The image’s observation vector is constructed in the same manner as for DWT, with the features being collected from each block in the image, from left to right and from top to bottom. This vector, along with the observation vectors from all other training images of the same individual, is used to train the HMM for this individual using maximum likelihood (ML) estimation. As the identification process assumes that all probe images belong to known individuals, the image is classified as the identity of the HMM that produces the highest likelihood value

Mathematical background

Problems assigned to a structural HMM

Data collection

Findings

CONCLUSION