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This study presents the first detailed study of total variability modelling (TVM) for face verification. TVM was originally proposed for speaker verification, where it has been accepted as state-of-the-art technology. Also referred to as front-end factor analysis, TVM uses a probabilistic model to represent a speech recording as a low-dimensional vector known as an `i-vector'. This representation has been successfully applied to a wide variety of speech-related pattern recognition applications, and remains a hot topic in biometrics. In this work, the authors extend the application of i-vectors beyond the domain of speech to a novel representation of facial images for the purpose of face verification. Extensive experimentation on several challenging and publicly available face recognition databases demonstrates that TVM generalises well to this modality, providing between 17 and 39% relative reduction in verification error rate compared to a baseline Gaussian mixture model system. Several i-vector session compensation and scoring techniques were evaluated including source-normalised linear discriminant analysis (SN-LDA), probabilistic LDA and within-class covariance normalisation. Finally, this study provides a detailed comparison of the complexity of TVM, highlighting some important computational advantages with respect to related state-of-the-art techniques.