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 of a matrix whose size grows quadratically with the number of samples for the identity (class). To date, two approaches have been taken to deal with this problem, to 1) use an exact solution that calculates this large matrix and is obviously not scalable with the number of samples or 2) derive a variational approximation to the problem. We present a scalable derivation which is theoretically equivalent to the previous nonscalable solution and thus obviates the need for a variational approximation. Experimentally, we demonstrate the efficacy of our approach in two ways. First, on labeled faces in the wild, we illustrate the equivalence of our scalable implementation with previously published work. Second, on the large Multi-PIE database, we illustrate the gain in performance when using more training samples per identity (class), which is made possible by the proposed scalable formulation of PLDA.

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. Secondly, efficient processing of large amounts of multimedia data is provided by fast C++ implementations of identified bottlenecks. The Python environment is integrated seamlessly with the C++ library, which ensures the library is easy to use and extensible. Thirdly, Bob supports reproducible research through its integrated experimental protocols for several databases. Finally, a strong emphasis is placed on code clarity, documentation, and thorough unit testing. Bob is thus an attractive resource for researchers due to this unique combination of ease of use, efficiency, extensibility and transparency. Bob is an open-source library and an ongoing community effort.

This paper applies score and feature normalization techniques to parts-based Gaussian mixture model (GMM) face authentication. In particular, we propose to utilize techniques that are well established in state-of-the-art speaker authentication, and apply them to the face authentication task. For score normalization, T-, Z- and ZT-norm techniques are evaluated. For feature normalization, we propose a generalization of feature warping to 2D images, which is applied to discrete cosine transform (DCT) features prior to modeling. Evaluation is performed on a range of challenging databases relevant to forensics and security, including surveillance and access control scenarios. The normalization techniques are shown to generalize well to the face authentication task, resulting in relative improvements in half total error rate (HTER) of between 17% and 62%.

This paper applies inter-session variability modelling and joint factor analysis to face authentication using Gaussian 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 authentication on the publicly-available BANCA, SCface and MO- BIO databases. We propose a face authentication protocol for the challenging SCface database, and provide the first results on the MOBIO still face protocol. The techniques provide relative reductions in error rate of up to 44%, using only limited training data. On the BANCA database, our results represent a 31% reduction in error rate when benchmarked against previous work.

This paper proposes to improve spoken term detection (STD) accuracy by optimizing the figure of merit (FOM). In this paper, the index takes the form of a phonetic posterior-feature matrix. Accuracy is improved by formulating STD as a discriminative training problem and directly optimizing the FOM, through its use as an objective function to train a transformation of the index. The outcome of indexing is then a matrix of enhanced posterior-features that are directly tailored for the STD task. The technique is shown to improve the FOM by up to 13% on held-out data. Additional analysis explores the effect of the technique on phone recognition accuracy, examines the actual values of the learned transform, and demonstrates that using an extended training data set results in further improvement in the FOM.

This paper introduces a novel technique to directly optimise the Figure of Merit (FOM) for phonetic spoken term detection. The FOM is a popular measure of STD accuracy, making it an ideal candidate for use as an objective function. A simple linear model is introduced to transform the phone log-posterior probabilities output by a phone classifier to produce enhanced log-posterior features that are more suitable for the STD task. Direct optimisation of the FOM is then performed by training the parameters of this model using a nonlinear gradient descent algorithm. Substantial FOM improvements of 11% relative are achieved on held-out evaluation data, demonstrating the generalisability of the approach.

While spoken term detection (STD) systems based on word indices provide good accuracy, there are several practical applications where it is infeasible or too costly to employ an LVCSR engine. An STD system is presented, which is designed to incorporate a fast phonetic decoding front-end and be robust to decoding errors whilst still allowing for rapid search speeds. This goal is achieved through monophone open-loop decoding coupled with fast hierarchical phone lattice search. Results demonstrate that an STD system that is designed with the constraint of a fast and simple phonetic decoding front-end requires a compromise to be made between search speed and search accuracy.

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 uses phonetic decoding and Dynamic Match Lattice Spotting to rapidly locate search terms, combined with a neural network-based verification stage. The use of phonetic search means the system is open vocabulary and performs usefully (Actual Term-Weighted Value of 0.23) whilst avoiding the cost of a large vocabulary speech recognition engine.