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The addition of Three Dimensional (3D) data has the potential to greatly improve the accuracy of Face Recognition Technologies by providing complementary information. In this paper a new method combining intensity and range images and providing insensitivity to expression variation based on Log-Gabor Templates is presented. By breaking a single image into(More)
This paper details the results of a Face Authentica-tion Test (FAT2004) [2] held in conjunction with the 17th International Conference on Pattern Recognition. The contest was held on the publicly available BANCA database [1] according to a defined protocol [7]. The competition also had a sequestered part in which institutions had to submit their algorithms(More)
This paper examines session variability modelling for face authentication using Gaussian mixture models. Session variability modelling aims to explicitly model and suppress detrimental within-class (inter-session) variation. We examine two techniques to do this, inter-session variability modelling (ISV) and joint factor analysis (JFA), which were initially(More)
Bob is a free signal processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, Switzerland. The toolbox is designed to meet the needs of researchers by reducing development time and efficiently processing data. Firstly, Bob provides a researcher-friendly Python environment for rapid development.(More)
This paper applies inter-session variability modelling and joint factor analysis to face authentication using Gaus-sian mixture models. These techniques, originally developed for speaker authentication, aim to explicitly model and remove detrimental within-client (inter-session) variation from client models. We apply the techniques to face authen-tication(More)
— This paper presents a novel fully automatic bi-modal, face and speaker, recognition system which runs in real-time on a mobile phone. The implemented system runs in real-time on a Nokia N900 and demonstrates the feasibility of performing both automatic face and speaker recognition on a mobile phone. We evaluate this recognition system on a novel(More)
In this paper, we use a hill-climbing attack algorithm based on Bayesian adaption to test the vulnerability of two face recognition systems to indirect attacks. The attacking technique uses the scores provided by the matcher to adapt a global distribution computed from an independent set of users, to the local specificities of the client being attacked. The(More)
—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 normal-isation, T-, Z-and ZT-norm techniques are(More)
In this paper, we present a scalable and exact solution for probabilistic linear discriminant analysis (PLDA). PLDA is a probabilistic model that has been shown to provide state-of-the-art performance for both face and speaker recognition. However, it has one major drawback: At training time estimating the latent variables requires the inversion and storage(More)
This paper evaluates the performance of face and speaker verification techniques in the context of a mobile environment. The mobile environment was chosen as it provides a realistic and challenging test-bed for biometric person verification techniques to operate. For instance the audio environment is quite noisy and there is limited control over the(More)