A Probabilistic Model for Face Transformation with Application to Person Identification

Florent Perronnin,Jean-Luc Dugelay,Kenneth Rose

doi:10.1155/s1110865704308012

Abstract

A novel approach for content-based image retrieval and its specialization to face recognition are described. While most face recognition techniques aim at modeling faces, our goal is to model the transformation between face images of the same person. As a global face transformation may be too complex to be modeled directly, it is approximated by a collection of local transformations with a constraint that imposes consistency between neighboring transformations. Local transformations and neighborhood constraints are embedded within a probabilistic framework using two-dimensional hidden Markov models (2D HMMs).We further introduce a new efficient technique, called turbo-HMM (T-HMM) for approximating intractable 2D HMMs. Experimental results on a face identification task show that our novel approach compares favorably to the popular eigenfaces and fisherfaces algorithms.

Highlights

Pattern classification is concerned with the general problem of inferring classes or “categories” from observations [1]
While most face recognition techniques aim at modeling faces, our goal is to model the transformation between face images of the same person
This paper introduces a novel approach for contentbased image retrieval, which is applied to face identification and whose stochastic model focuses on the relation between observations of the same class rather than the generation process

Summary

Introduction

Pattern classification is concerned with the general problem of inferring classes or “categories” from observations [1]. The success of a pattern classification system is largely dependent on the quality of its stochastic model, which generally models the generation of observations, to capture the intraclass variability. Most face recognition algorithms attempt to build for each person P a face model ᏹp (the stochastic source of the system) which is designed to describe as accurately as possible his/her intraface variability. This paper introduces a novel approach for contentbased image retrieval, which is applied to face identification and whose stochastic model focuses on the relation between observations of the same class rather than the generation process. We attempt to model a transformation between face images of the same person. A similar approach for general content-based image retrieval appeared first in [4] and preliminary results were presented on a database of binary images

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