Learn More
We consider multi-agent systems with preferences, which are just standard multi-agent systems, except that each agent can set some preferences over its local data. This makes these systems more flexible and realistic, since it is possible to represent possibilities, costs, probabilities, preferences, and penalties. However, it also transforms the search for(More)
This paper details the submission from the Speech and Audio Research Lab of Queensland University of Technology (QUT) to the inaugural 2006 NIST Spoken Term Detection Evaluation. The task involved accurately locating the occurrences of a specified list of English terms in a given corpus of broadcast news and conversational telephone speech. The QUT system(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)
—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)
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)
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)
In this paper we introduce the facereclib, the first software library that allows to compare a variety of face recognition algorithms on most of the known facial image databases and that permits rapid proto-typing of novel ideas and testing of meta-parameters of face recognition algorithms. The facereclib is built on the open source signal processing and(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)