3D face recognition in presence of expressions by fusion regions of interest

We propose a face recognition system insensitive to expressions. This system uses the fusion by concatenating the entire face with the regions of interest (nose, mouth, right eye and left eye). To enhance the discriminant information phases of Gabor filter are used. The Principal Component Analysis (PCA) + Enhanced Fisher linear discriminant Model (EFM) are applied to the data to find a reduced basis projection and discriminant. The classification is usually performed using a single distance measure in the final multidimensional space. In this work we use a support vector machine (SVM) architecture with one against all. The model is studied and applied to the CASIA color database and gives a recognition rate of overall evaluation RR_eval = 94.30% and the test set RR_test = 81.30%.