Likelihood normalization for face authentication in variable recording conditions

In this paper we evaluate the effectiveness of two likelihood normalization techniques, the background model set (BMS) and the universal background model (UBM), for improving performance and robustness of four face authentication systems utilizing a Gaussian mixture model (GMM) classifier. The systems differ in the feature extraction method used: eigenfaces (PCA), 2-D DCT, 2-D Gabor wavelets and DCT-mod2. Experiments on the VidTIMIT database, using test images corrupted either by an illumination change or compression artefacts, suggest that likelihood normalization has little effect when using PCA derived features, while providing significant performance improvements when using the remaining features.