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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)
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)
I4U is a joint entry of nine research Institutes and Universities across 4 continents to NIST SRE 2012. It started with a brief discussion during the Odyssey 2012 workshop in Singapore. An online discussion group was soon set up, providing a discussion platform for different issues surrounding NIST SRE'12. Noisy test segments, uneven multi-session training,(More)
This paper examines the issue of face, speaker and bi-modal authentication in mobile environments when there is significant condition mismatch. We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biomet-ric samples acquired with a mobile phone. To(More)
This paper evaluates the performance of the twelve primary systems submitted to the evaluation on speaker verification in the context of a mobile environment using the MOBIO database. The mobile environment provides a challenging and realistic test-bed for current state-of-the-art speaker verification techniques. Results in terms of equal error rate (EER),(More)
The locations of the eyes are the most commonly used features to perform face normalization (i. e., alignment of facial features), which is an essential preprocessing stage of many face recognition systems. In this paper, we study the sensitivity of open source implementations of five face recognition algorithms to misalignment caused by eye localization(More)
The MOBIO database provides a challenging test-bed for speaker and face recognition systems because it includes voice and face samples as they would appear in forensic scenarios. In this paper, we investigate uni-modal and bi-modal multi-algorithm fusion using logistic regression. The source speaker and face recognition systems were taken from the 2013(More)
In this paper, we introduce Spear, an open source and ex-tensible toolbox for state-of-the-art speaker recognition. This toolbox is built on top of Bob, a free signal processing and machine learning library. Spear implements a set of complete speaker recognition toolchains, including all the processing stages from the front-end feature extractor to the(More)
—In this paper, we present the Idiap Research Institute submission to the 2012 NIST Speaker Recognition Evaluation. Our system is based on the Inter-Session Variability (ISV) modelling technique. The implementation of the system relies on Bob, a free signal processing and machine learning toolbox developed at Idiap. The NIST official results show the(More)