A Statistical Discriminant Model for Face Interpretation and Reconstruction

Lacunaridade Aplicada em Análise de Textura

Edson C. KitaniCarlos E. ThomazDuncan Fyfe Gillies

André Ricardo BackesOdemir Martinez Bruno

Multivariate statistical approaches have played an important role of recognising face images and characterizing their differences. In this paper, we introduce the idea of using a two-stage separating hyper-plane, here called Statistical Discriminant Model (SDM), to interpret and reconstruct face images. Analogously to the well-known Active Appearance Model proposed by Cootes et. al, SDM requires a previous alignment of all the images to a common template to minimise variations that are not necessarily related to differences between the faces. However, instead of using landmarks or annotations on the images, SDM is based on the idea of using PCA to reduce the dimensionality of the original images and a maximum uncertainty linear classifier (MLDA) to characterise the most discriminant changes between the groups of images. The experimental results based on frontal face images indicate that the SDM approach provides an intuitive interpretation of the differences between groups, reconstructing characteristics that are very subjective in human beings, such as beauty and happiness. This article presents a study about lacunarity applied to texture analysis. Lacunarity is a counterpart to the fractal dimension that describes the texture of a fractal. In this work this concept is adapted for texture analysis, more specifically to the problem of pattern recognition, where lacunarity is considered as a texture signature and could be used to quantify and to classify a texture. The work still presents an experiment that illustrates the potential of the technique for the pattern recognition using the texture attribute.

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