Cross-Pollination of Normalization Techniques From Speaker to Face Authentication Using Gaussian Mixture Models

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