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

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.

An Automatic Framework for Segmentation and Digital Inpainting of 2D Frontal Face Images

“…A partir de uma população amostral de N imagens frontais com expressão facial neutra e normalizadas espacialmente [34], calcula-se a imagem média dessas amostras através da equação:…”

“…Há rasuras como borrão, cabelo sobre o rosto, armação de óculos, sorriso, chupeta, baixa qualidade de resolução e carimbo. Todas as im-agens utilizadas estão de acordo com os padrões internacionais ANSI [37] e ISO [38] de iluminação, background, posição e expressão e foram normalizadas e equalizadas previamente [34].…”