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A Multi-Linear Statistical Method for Discriminant Analysis of 2D Frontal Face Images

A Multi-Linear Statistical Method for Discriminant Analysis of 2D Frontal Face Images
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Author(s): Carlos Eduardo Thomaz (Centro Universitário da FEI (FEI), Brazil), Vagner do Amaral (Centro Universitário da FEI (FEI), Brazil), Gilson Antonio Giraldi (Laboratório Nacional de Computação Científica (LNCC), Brazil), Edson Caoru Kitani (Universidade de São Paulo (USP), Brazil), João Ricardo Sato (Universidade Federal do ABC (UFABC), Brazil) and Duncan Gillies (Imperial College London, UK)
Copyright: 2012
Pages: 16
Source title: Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies
Source Author(s)/Editor(s): Vijay Kumar Mago (Simon Fraser University, Canada) and Nitin Bhatia (DAV College, India)
DOI: 10.4018/978-1-61350-429-1.ch002

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Abstract

This chapter describes a multi-linear discriminant method of constructing and quantifying statistically significant changes on human identity photographs. The approach is based on a general multivariate two-stage linear framework that addresses the small sample size problem in high-dimensional spaces. Starting with a 2D data set of frontal face images, the authors determine a most characteristic direction of change by organizing the data according to the patterns of interest. These experiments on publicly available face image sets show that the multi-linear approach does produce visually plausible results for gender, facial expression and aging facial changes in a simple and efficient way. The authors believe that such approach could be widely applied for modeling and reconstruction in face recognition and possibly in identifying subjects after a lapse of time.

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