Robust Face Recognition Using Local Gradient Probabilistic Pattern (LGPP)

In this work, we propose a new local pattern for face recognition, named Local Gradient Probabilistic Pattern (LGPP). It is an extension of Local Gradient Pattern (LGP) that uses a very important result of probability theory; it is the law large numbers. In this direction, the distribution of the gray levels on a face image follows a law of probability, which is the sum of several normal laws. The current pixel will be evaluated by the confidence interval concept. The suggested model is merged with the most known algorithms of data analysis in the face recognition field, in particular the PCA, the LDA, the 2DPCA and the 2DLDA. The tests carried out on the ORL and YALE databases show the effectiveness of LGPP+2DPCA and LGPP+2DLDA systems. The experimental exactitude is of 96 %.