Feature extraction and face verification using gabor and gaussian mixture models

Abstract

This paper proposes a faces verification in which the feature extraction is carried out using the discrete Gabor function (DGF), while the Gaussian Mixture Model (GMM) is used in the face verification stage. Evaluation results using standard data bases with different parameters, such as the number of mixtures and the number of face used for training show that proposed system provides better results that other proposed systems with a correct verification rate larger than 95%. Although, as happens in must face recognition systems, the verification rate decreases when the target faces present some rotation degrees. © Springer-Verlag Berlin Heidelberg 2007.