Cross-pollination of normalisation techniques from speaker to face authentication using Gaussian mixture models

This paper applies score and feature normalisation techniques to parts-based Gaussian mixture model (GMM) face authentication. In particular, we propose to utilise techniques that are well established in state-of-the-art speaker authentication, and apply them to the face authentication task. For score normalisation, T-, Zand ZT-norm techniques are evaluated. For feature normalisation, we propose a generalisation of feature warping to 2D images, which is applied to discrete cosine transform (DCT) features prior to modelling. Evaluation is performed on a range of challenging databases relevant to forensics and security, including surveillance and access control scenarios. The normalisation techniques are shown to generalise well to the face authentication task, resulting in relative improvements in half total error rate (HTER) of between 17% and 62%.