GMM-based SVM for face recognition

Abstract

A new face recognition algorithm is presented. It supposes that a video sequence of a person is available both at enrollment and test time. During enrollment, a client Gaussian mixture model (GMM) is adapted from a world GMM using eigenface features extracted from each frame of the video. Then, a support vector machine (SVM) is used to find a decision border between the client GMM and pseudo-impostors GMMs. At test time, a GMM is adapted from the test video and a decision is taken using the previously learned client SVM. This algorithm brings a 3.5% equal error rate (EER) improvement over the biosecure reference system on the Pooled protocol of the BANCA database