Laurent El Shafey

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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)
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)
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 process of manually labeling data is very expensive and sometimes infeasible due to privacy and security issues. This paper investigates the use of two algorithms for clustering unlabeled training i-vectors. This aims at improving speaker recognition performance by using state-of-the-art supervised techniques in the context of the NIST i-vector Machine(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)
In this paper, we introduce Spear, an open source and extensible 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 final(More)
One important type of biometric authentication is face recognition, a research area of high popularity with a wide spectrum of approaches that have been proposed in the last few decades. The majority of existing approaches are conceived for or evaluated on constrained still images. However, more recently research interests have shifted towards unconstrained(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 biometric samples acquired with a mobile phone. To(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)