Symbolic factorial discriminant analysis for face recognition under variable lighting
P. S. Hiremath, C. J. Prabhakar · 2006
In this paper, a new appearance-based technique called symbolic factorial discriminant analysis (SFDA) is explored for face representation and recognition under varying illumination conditions. In the past few years, many appearance-based methods have been proposed to model image variations of human faces under different lighting conditions using single valued variables to represent the facial features. In the proposed symbolic factorial discriminant analysis method, the authors extract interval type discriminating features, which are robust due to illumination changes. The minimum distance classifier with symbolic dissimilarity measure is used for classification. The experimental results have demonstrated that the performance of this algorithm is much better than the other algorithms on Yale face database B.