Aurélien Mayoue

Fingerprint and On-Line Signature Verification Competitions at ICB 2009

This paper describes the objectives, the tasks proposed to the participants and the associated protocols in terms of database and assessment tools of two competitions on fingerprints and on-line signatures. The particularity of the fingerprint competition is to be an on-line competition, for evaluation of fingerprint verification tools such as minutiae extractors and matchers as well as complete systems. This competition will be officialy launched during the ICB conference. The on-line signature competition will test the influence of multi-sessions, environmental conditions (still and mobility) and signature complexity on the performance of complete systems using two datasets extracted from the BioSecure database. Its result will be presented during the ICB conference.

Shift & 2D Rotation Invariant Sparse Coding for Multivariate Signals

Classical dictionary learning algorithms (DLA) allow unicomponent signals to be processed. Due to our interest in two-dimensional (2D) motion signals, we wanted to mix the two components to provide rotation invariance. So, multicomponent frameworks are examined here. In contrast to the well-known multichannel framework, a multivariate framework is first introduced as a tool to easily solve our problem and to preserve the data structure. Within this multivariate framework, we then present sparse coding methods: multivariate orthogonal matching pursuit (M-OMP), which provides sparse approximation for multivariate signals, and multivariate DLA (M-DLA), which empirically learns the characteristic patterns (or features) that are associated to a multivariate signals set, and combines shift-invariance and online learning. Once the multivariate dictionary is learned, any signal of this considered set can be approximated sparsely. This multivariate framework is introduced to simply present the 2D rotation invariant (2DRI) case. By studying 2D motions that are acquired in bivariate real signals, we want the decompositions to be independent of the orientation of the movement execution in the 2D space. The methods are thus specified for the 2DRI case to be robust to any rotation: 2DRI-OMP and 2DRI-DLA. Shift and rotation invariant cases induce a compact learned dictionary and provide robust decomposition. As validation, our methods are applied to 2D handwritten data to extract the elementary features of this signals set, and to provide rotation invariant decomposition.

Some results from the biosecure talking face evaluation campaign

The BioSecure Network of Excellence has collected a large multi- biometric publicly available database and organized the BioSecure Multimodal Evaluation Campaigns (BMEC) in 20072. This paper reports on the Talking Faces campaign. Open source reference systems were made available to participants and four laboratories submitted executable code to the organizer who performed tests on sequestered data. Several deliberate impostures were tested. It is demonstrated that forgeries are a real threat for such systems. A technological race is ongoing between deliberate impostors and system developers.

Text-independent Speaker Verification

In this chapter, an overview of text-independent speaker verification is given first. Then, recent developments needed to reach state-of-the-art performances using low-level (acoustic) features as well as how to use complementary high-level information, are presented. The most relevant speaker verification evaluation campaigns and databases are also summarized. The BioSecure benchmarking framework for speaker verification using open-source state-of-the-art algorithms, well-known databases, and reference protocols is presented after. It is also shown how to reach state-of-the-art performances using open-source software with a case study example on the National Institute of Standards and Technology 2005 Speaker Evaluation data (NIST’2005 SRE). The examples of key factors influencing the performances of speaker verification experiments on the NIST’2005 evaluation data are grouped in three parts. The first set of experiments is related to the importance of front-end processing and data selection to fine-tune the acoustic Gaussian Mixture systems. The second set of experiments illustrates the importance of speaker and session variability modeling methods in order to cope with mismatched enrollment/test conditions. The third series of experiments demonstrates the usefulness of data-driven speech segmentation methods for extracting complementary high-level information. The chapter ends with conclusions and perspectives.

Preprocessing for classification of sparse data: Application to trajectory recognition

On one hand, sparse coding, which is widely used in signal processing, consists of representing signals as linear combinations of few elementary patterns selected from a dedicated dictionary. The output is a sparse vector containing few coding coefficients and is called sparse code. On the other hand, Multilayer Perceptron (MLP) is a neural network classification method that learns non linear borders between classes using labeled data examples. The MLP input data are vectors, usually normalized and preprocessed to minimize the inter-class correlation. This article acts as a link between sparse coding and MLP by converting sparse code into convenient vectors for MLP input. This original association assures in this way the classification of any sparse signals. Experimental results obtained by the whole process on trajectories data and comparisons to other methods show that this approach is efficient for signals classification.

BioSecure Multimodal Evaluation Campaign 2007 (BMEC’2007)

This chapter presents the experimental results from the mobile scenario of the BioSecure Multimodal Evaluation Campaign 2007 (BMEC’2007). This competition was organized by the BioSecure Network of Excellence (NoE) and aimed at testing the robustness of monomodal and multimodal biometric verification systems to degraded acquisition conditions. The database used for the evaluation is the large-scale multimodal database acquired in the framework of the BioSecure NoE in mobility conditions. During this evaluation, the BioSecure benchmarking methodology was followed to enable a fair comparison of the submitted algorithms. In this way, we believe that the BMEC’2007 database and results will be useful both to the participants and, more generally, to all practitioners in the field as a benchmark for improving methods and for enabling evaluation of algorithms.

Recursive Least Squares algorithm dedicated to early recognition of explosive compounds thanks to multi-technology sensors

In this paper, a novel gas identification approach based on the Recursive Least Squares (RLS) algorithm is proposed. We detail some adaptations of RLS to be applied to a sensor matrix of several technologies in optimal conditions. The low complexity of the algorithm and its ability to process online samples from multi-sensor make the real-time identification of volatile compounds possible. The effectiveness of this approach to early detect and recognize explosive compounds in the air has been successfully demonstrated on an experimentally obtained dataset.

Multivariate dictionary learning and shift & 2D rotation invariant sparse coding

In this article, we present a new tool for sparse coding : Multivariate DLA which empirically learns the characteristic patterns associated to a multivariate signals set. Once learned, Multivariate OMP approximates sparsely any signal of this considered set. These methods are specified to the 2D rotation-invariant case. Shift and rotation-invariant cases induce a compact learned dictionary. Our methods are applied to 2D handwritten data in order to extract the elementary features of this signals set.

The BioSecure Benchmarking Methodology for Biometric Performance Evaluation

Measuring real progress achieved with new research methods and pinpointing the unsolved problems is only possible within a well defined evaluation methodology. This point is even more crucial in the field of biometrics, where development and evaluation of new biometric techniques are challenging research areas. Such an evaluation methodology is developed and put in practice in the European Network of Excellence (NoE) BioSecure. Its key elements are: open-source software, publicly available biometric databases, well defined evaluation protocols, and additional information (such as How-to documents) that allow the reproducibility of the proposed benchmarking experiments. As of this writing, such a framework is available for eight biometric modalities: iris, fingerprint, online handwritten signature, hand geometry, speech, 2D and 3D face, and talking faces. In this chapter we first present the motivations that lead us to the proposed evaluation methodology. A brief description of the proposed evaluation tools follows. The multiple possibilities of how this evaluation methodology can be used are also described, and introduce the other chapters of this book that illustrate how the proposed benchmarking methodology can be put into practice.

Open Source Reference Systems for Biometric Verification of Identity

This paper focuses on the common evaluation framework which was developed by the BioSecure Network of Excellence during the European FP6 project BioSecure (Biometrics for Secure authentication). This framework, which is composed of open-source reference systems, publicly available databases, assessment protocols and benchmarking results, introduces a new experimental methodology for conducting, reporting and comparing experiments for biometric systems, participating to standardisation efforts. Its use will permit to make a link between different published works. It provides also the necessary tools to assure the reproducibility of the benchmarking biometric experiments. This framework can be considered as a re-liable and innovative process to evaluate the progress of research in the field of bio-metrics.