Jean Hennebert

The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)

A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1 over the Internet, 2 in an office environment with desktop PC, and 3 in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisition sessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of either monomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to be available for research purposes through the BioSecure Association during 2008.

Combined Handwriting and Speech Modalities for User Authentication

In this paper, we report on the development of an efficient user authentication system based on a combined acquisition of online pen and speech signals. The novelty of our approach is in the simultaneous recording of these two modalities, simply asking the user to utter what she/he is writing. The main benefit of this multimodal approach is a better accuracy at no extra costs in terms of access time or inconvenience. Another benefit comes from an increased difficulty for forgers willing to perform imitation attacks as two signals need to be reproduced. We are comparing here two potential scenarios of use. The first one is called spoken signatures where the user signs and says the content of the signature. The second scenario is based on spoken handwriting where the user is prompted to write and read the content of sentences randomly extracted from a text. Data according to these two scenarios have been recorded from a set of 70 users. In the first part of this paper, we describe the acquisition procedure, and we comment on the viability and usability of such simultaneous recordings. Our conclusions are supported by a short survey performed with the users. In the second part, we present the authentication systems that we have developed for both scenarios. More specifically, our strategy was to model independently both streams of data and to perform a fusion at the score level. Starting from a state-of-the-art-modeling algorithm based on Gaussian Mixture Models trained with an Expectation-Maximization procedure, we report on several significant improvements that are brought. As a general observation, the use of both modalities outperforms significantly the modalities used alone.

Page segmentation of historical document images with convolutional autoencoders

In this paper, we present an unsupervised feature learning method for page segmentation of historical handwritten documents available as color images. We consider page segmentation as a pixel labeling problem, i.e., each pixel is classified as either periphery, background, text block, or decoration. Traditional methods in this area rely on carefully hand-crafted features or large amounts of prior knowledge. In contrast, we apply convolutional autoencoders to learn features directly from pixel intensity values. Then, using these features to train an SVM, we achieve high quality segmentation without any assumption of specific topologies and shapes. Experiments on three public datasets demonstrate the effectiveness and superiority of the proposed approach.

A Survey on Intrusive Load Monitoring for Appliance Recognition

Electricity load monitoring of appliances has become an important task considering the recent economic and ecological trends. In this game, machine learning has an important part to play, allowing for energy consumption understanding, critical equipment monitoring and even human activity recognition. This paper provides a survey of current researches on Intrusive Load Monitoring (ILM) techniques. ILM relies on low-end electricity meter devices spread inside the habitations, as opposed to Non-Intrusive Load Monitoring (NILM) that relies on an unique point of measurement, the smart meter. Potential applications and principles of ILMs are presented and compared to NILM. A focus is also given on feature extraction and machine learning algorithms typically used for ILM applications.

Machine learning approaches for electric appliance classification

We report on the development of an innovative system which can automatically recognize home appliances based on their electric consumption profiles. The purpose of our system is to apply adequate rules to control electric appliance in order to save energy and money. The novelty of our approach is in the use of plug-based low-end sensors that measure the electric consumption at low frequency, typically every 10 seconds. Another novelty is the use of machine learning approaches to perform the classification of the appliances. In this paper, we present the system architecture, the data acquisition protocol and the evaluation framework. More details are also given on the feature extraction and classification models being used. The evaluation showed promising results with a correct rate of identification of 85%.

A new forgery scenario based on regaining dynamics of signature

We present in this paper a new forgery scenario for dynamic signature verification systems. In this scenario, we assume that the forger has got access to a static version of the genuine signature, is using a dedicated software to automatically recover dynamics of the signature and is using these regained signatures to break the verification system. We also show that automated procedures can be built to regain signature dynamics, making some simple assumptions on how signatures are performed. We finally report on the evaluation of these procedures on the MCYT-100 signature database on which regained versions of the signatures are generated. This set of regained signatures is used to evaluate the rejection performance of a baseline dynamic signature verification system. Results show that the regained forgeries generate much more false acceptation in comparison to the random and low-force forgeries available in the MCYT-100 database. These results clearly show that such kind of forgery attacks can potentially represent a critical security breach for signature verification systems.

Page Segmentation for Historical Handwritten Document Images Using Color and Texture Features

In this paper we present a physical structure detection method for historical handwritten document images. We considered layout analysis as a pixel labeling problem. By classifying each pixel as either periphery, background, text block, or decoration, we achieve high quality segmentation without any assumption of specific topologies and shapes. Various color and texture features such as color variance, smoothness, Laplacian, Local Binary Patterns, and Gabor Dominant Orientation Histogram are used for classification. Some of these features have so far not got many attentions for document image layout analysis. By applying an Improved Fast Correlation-Based Filter feature selection algorithm, the redundant and irrelevant features are removed. Finally, the segmentation results are refined by a smoothing post-processing procedure. The proposed method is demonstrated by experiments conducted on three different historical handwritten document image datasets. Experiments show the benefit of combining various color and texture features for classification. The results also show the advantage of using a feature selection method to choose optimal feature subset. By applying the proposed method we achieve superior accuracy compared with earlier work on several datasets, e.g., We achieved 93% accuracy compared with 91% of the previous method on the Parzival dataset which contains about 100 million pixels.

Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms

OBJECTIVE This research is motivated by the issue of classifying illnesses of chronically ill patients for decision support in clinical settings. Our main objective is to propose multi-label classification of multivariate time series contained in medical records of chronically ill patients, by means of quantization methods, such as bag of words (BoW), and multi-label classification algorithms. Our second objective is to compare supervised dimensionality reduction techniques to state-of-the-art multi-label classification algorithms. The hypothesis is that kernel methods and locality preserving projections make such algorithms good candidates to study multi-label medical time series. METHODS We combine BoW and supervised dimensionality reduction algorithms to perform multi-label classification on health records of chronically ill patients. The considered algorithms are compared with state-of-the-art multi-label classifiers in two real world datasets. Portavita dataset contains 525 diabetes type 2 (DT2) patients, with co-morbidities of DT2 such as hypertension, dyslipidemia, and microvascular or macrovascular issues. MIMIC II dataset contains 2635 patients affected by thyroid disease, diabetes mellitus, lipoid metabolism disease, fluid electrolyte disease, hypertensive disease, thrombosis, hypotension, chronic obstructive pulmonary disease (COPD), liver disease and kidney disease. The algorithms are evaluated using multi-label evaluation metrics such as hamming loss, one error, coverage, ranking loss, and average precision. RESULTS Non-linear dimensionality reduction approaches behave well on medical time series quantized using the BoW algorithm, with results comparable to state-of-the-art multi-label classification algorithms. Chaining the projected features has a positive impact on the performance of the algorithm with respect to pure binary relevance approaches. CONCLUSIONS The evaluation highlights the feasibility of representing medical health records using the BoW for multi-label classification tasks. The study also highlights that dimensionality reduction algorithms based on kernel methods, locality preserving projections or both are good candidates to deal with multi-label classification tasks in medical time series with many missing values and high label density.

ICDAR 2011 - Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text

This paper describes the Arabic Recognition Competition: Multi-font Multi-size Digitally Represented Text held in the context of the 11

Robust Text Line Segmentation for Historical Manuscript Images Using Color and Texture

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