Johnny Mariéthoz

The BANCA database and evaluation protocol

In this paper we describe the acquisition and content of a new large, realistic and challenging multi-modal database intended for training and testing multi-modal verification systems. The BANCA database was captured in four European languages in two modalities (face and voice). For recording, both high and low quality microphones and cameras were used. The subjects were recorded in three different scenarios, controlled, degraded and adverse over a period of three months. In total 208 people were captured, half men and half women. In this paper we also describe a protocol for evaluating verification algorithms on the database. The database will be made available to the research community through http://www.ee.surrey.ac.uk/Research/VSSP/banca.

Confidence Measures for Multimodal Identity Verification

Abstract Multimodal fusion for identity verification has already shown great improvement compared to unimodal algorithms. In this paper, we propose to integrate confidence measures during the fusion process. We present a comparison of three different methods to generate such confidence information from unimodal identity verification systems. These methods can be used either to enhance the performance of a multimodal fusion algorithm or to obtain a confidence level on the decisions taken by the system. All the algorithms are compared on the same benchmark database, namely XM2VTS, containing both speech and face information. Results show that some confidence measures did improve statistically significantly the performance, while other measures produced reliable confidence levels over the fusion decisions.

Measuring the Performance of Face Localization Systems

The purpose of Face localization is to determine the coordinates of a face in a given image. It is a fundamental research area in computer vision because it serves, as a necessary first step, any face processing systems, such as automatic face recognition, face tracking or expression analysis. Most of these techniques assume, in general, that the face region has been perfectly localized. Therefore, their performances depend widely on the accuracy of the face localization process. The purpose of this paper is to mainly show that the error made during the localization process may have different impacts which depend on the final application. We first show the influence of localization errors on the specific task of face verification and then empirically demonstrate the problems of current localization performance measures when applied to this task. In order to properly evaluate the performance of a face localization algorithm, we then propose to embed the final application (here face verification) into the performance measuring process. Using two benchmark databases, BANCA and XM2VTS, we proceed by showing empirically that our proposed method to evaluate localization algorithms better matches the final verification performance.

Learning the decision function for speaker verification

Explores the possibility of replacing the usual thresholding decision rule of log likelihood ratios used in speaker verification systems by more complex and discriminant decision functions based for instance on linear regression models or support vector machines. Current speaker verification systems, based on generative models such as HMMs or Gaussian mixture models, can indeed easily be adapted to use such decision functions. Experiments on both text dependent and text independent tasks always yielded performance improvements and sometimes significantly.

Writer identification for smart meeting room systems

In this paper we present a text independent on-line writer identification system based on Gaussian Mixture Models (GMMs). This system has been developed in the context of research on Smart Meeting Rooms. The GMMs in our system are trained using two sets of features extracted from a text line. The first feature set is similar to feature sets used in signature verification systems before. It consists of information gathered for each recorded point of the handwriting, while the second feature set contains features extracted from each stroke. While both feature sets perform very favorably, the stroke-based feature set outperforms the point-based feature set in our experiments. We achieve a writer identification rate of 100% for writer sets with up to 100 writers. Increasing the number of writers to 200, the identification rate decreases to 94.75%.

A unified framework for score normalization techniques applied to text-independent speaker verification

The purpose of this letter is to unify several of the state-of-the-art score normalization techniques applied to text-independent speaker verification systems. We propose a new framework for this purpose. The two well-known Z- and T-normalization techniques can be easily interpreted in this framework as different ways to estimate score distributions. This is useful since it helps to understand the various assumptions behind these well-known score normalization techniques and opens the door for yet more complex solutions. Finally, some experiments on the Switchboard database are performed in order to illustrate the validity of the new proposed framework.

Estimating the quality of face localization for face verification

Face localization is the process of finding the exact position of a face in a given image. This can be useful in several applications such as face tracking or person authentication. The purpose of this paper is to show that the error made during the localization process may have different impacts depending on the final application. Hence in order to evaluate the performance of a face localization algorithm, we propose to embed the final application (here face verification) into the performance measuring process. Moreover, in this paper, we estimate this embedding using either a multilayer perceptron or a k-nearest neighbor algorithm in order to speedup the evaluation process. We show on the BANCA database that our proposed measure best matches the final verification results when comparing several localization algorithms, on various performance measures currently used in face localization.

Speech & face based biometric authentication at IDIAP

We present an overview of research at IDIAP on speech & face based biometric authentication. This paper covers user-customised passwords, adaptation techniques, confidence measures (for use in fusion of audio & visual scores), face verification in difficult image conditions, as well as other related research issues. We also overviewed the open source Torch library, which has aided in the implementation of the above mentioned techniques.

Biometric person authentication is a multiple classifier problem

Several papers have already shown the interest of using multiple classifiers in order to enhance the performance of biometric person authentication systems. In this paper, we would like to argue that the core task of Biometric Person Authentication is actually a multiple classifier problem as such: indeed, in order to reach state-of-the-art performance, we argue that all current systems, in one way or another, try to solve several tasks simultaneously and that without such joint training (or sharing), they would not succeed as well. We explain hereafter this perspective, and according to it, we propose some ways to take advantage of it, ranging from more parameter sharing to similarity learning.

Voice-B system

In this paper, we evaluate a speaker verification system in the framework of a common project between IDIAP and ASCOM. The system used is based on new text-dependent speaker verification technologies. The validation has been done on a telephone speech database that contains more than one hundred speakers and the results obtained are between 2% and 4% half total error rate (HTER), which is acceptable for many applications.