A Multi-Linear Statistical Method for Discriminant Analysis of 2D Frontal Face Images
Carlos Eduardo Thomaz, Vagner do Amaral, Gilson Antonio Giraldi, Edson Caoru Kitani, João Ricardo Sato, Duncan Fyfe Gillies · Advances in computational intelligence and robotics book series · 2012
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.