Current models for predicting waitlist mortality among liver transplant (LT) candidates primarily rely on conventional statistical regression-based approaches, which are typically developed separately for hepatocellular carcinoma (HCC) and non-HCC patients. These linear models may fail to capture complex, nonlinear relationships in the data, limiting their predictive performance. In this study, we evaluate and compare existing clinical scoring systems against various machine learning (ML) models, including both linear and nonlinear approaches, with a particular focus on a Multi-Task Learning (MTL) framework. Our results demonstrate that MTL outperforms both conventional methods and single-task learners across HCC and non-HCC groups. Furthermore, by leveraging SHapley Additive Explanations (SHAP), we provide deeper insights into the MTL model's decision-making process, offering both global and local explanations while pinpointing key risk factors for waitlist mortality in both patient groups. This study highlights the potential of advanced ML methodologies to improve LT organ allocation and underscores the need for their broader adoption in clinical practice, pending prospective validation.

In this paper, we study the impact of uncontrolled mobile conditions on signatures, when signing on two touch-screen devices of different size. 74 persons captured their signatures on both an iPad and an iPhone with different signing areas. The study exploited several quantitative indicators: an intra-personal variability measure, a statistical quality measure, and verification performance with two classifiers. We show that for 69% of writers, their signatures are more complex when made on the large surface of the iPad. This result reflects that the majority of writers are less comfortable with signing on a small handheld device with a very constrained signing surface; besides, the used rubber-tipped stylus does not offer an optimal writing precision regarding the constrained signing surface. For the remaining writers, results show that when signing on the iPad, writers tend to fill out, spontaneously, the whole available space; this leads to information loss reflected by a reduced signature complexity and stability. Performance assessment shows a significant degradation when test and reference signatures are not captured on the same platform. When test and reference signatures are captured on the same platform, performance is better when the writer signs on the small surface available on the iPhone.

We propose an original approach for characterizing early Alzheimer, based on the analysis of online handwritten cursive loops. Unlike the literature, we model the loop velocity trajectory (full dynamics) in an unsupervised way. Through a temporal clustering based on K-medoids, with dynamic time warping as dissimilarity measure, we uncover clusters that give new insights on the problem. For classification, we consider a Bayesian formalism that aggregates the contributions of the clusters, by probabilistically combining the discriminative power of each. On a dataset consisting of two cognitive profiles, early-stage Alzheimer disease and healthy persons, each comprising 27 persons collected at Broca Hospital in Paris, our classification performance significantly outperforms the state-of-the-art, based on global kinematic features.

In this paper, we present the main results of the BioSecure Signature Evaluation Campaign (ESRA'2011). The objective of ESRA'2011 is to evaluate through two different tasks the resistance of different online signature systems to skilled forgeries categorized automatically according to their quality. Task 1 aims at studying with only coordinate time functions the influence of acquisition conditions (digitizing tablet vs. PDA) on systems' performance. The two BioSecure Data Sets DS2 and DS3 make this possible, since they contain data from the same 382 people, acquired respectively on a digitizer and on a PDA. Task 2 then aims at assessing the contribution of the five time functions available on a digitizer (coordinates, pressure, pen inclination) on systems' resistance to different qualities of skilled forgeries. Results of the 13 systems involved in this competition are reported and analyzed for both tasks in this paper. We observe that the best system in terms of performance on forgeries of "bad" quality is not necessarily the most resistant to an increased quality of skilled forgeries. Also, we note that mobile conditions are still threatening independently of the quality of forgeries. Finally, when adding pen inclination time functions to pressure and coordinates, we find that the gap between systems in terms of performance is wider than when only pen coordinates and pressure are considered.

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.

In this paper, we present a new phase-correlation-based iris matching approach in order to deal with degradations in iris images due to unconstrained acquisition procedures. Our matching system is a fusion of global and local Gabor phase-correlation schemes. The main originality of our local approach is that we do not only consider the correlation peak amplitudes but also their locations in different regions of the images. Results on several degraded databases, namely, the CASIA-BIOSECURE and Iris Challenge Evaluation 2005 databases, show the improvement of our method compared to two available reference systems, Masek and Open Source for Iris (OSRIS), in verification mode.

We present the general principles of a dynamical sequential fusion strategy for multibiometric systems allowing reducing the cost associated to the use of different modalities while preserving a good performance compared to that of a global fusion system. We have implemented this strategy in the BMEC (BioSecure multimodal evaluation campaign) 2007 cost-sensitive evaluation. We show that this approach was very robust to the occurrence of missing data thanks to its adaptive implementation.

This paper describes a system using two complementary sorts of information issuing from a hidden Markov model (HMM) for online signature verification. At each point of the signature, 25 features are extracted. These features are normalized before training and testing in order to improve the performance of the system. This normalization is writer-dependent; it exploits only five genuine signatures used to train the writer HMM. A claimed identity is confirmed when the arithmetic mean of two similarity scores, obtained on an input signature, is higher than a threshold. The first score is related to the likelihood given by the HMM of the claimed identity; the second score is related to the segmentation given by such an HMM on the input signature. A personalized score normalization is also proposed before fusion. Our approach is evaluated on several online signature databases, such as BIOMET, PHILIPS, MCYT, and SVC2004, which were captured under different acquisition conditions. For the first time in signature verification, we show that the fusion of segmentation-based information generated by the HMM with likelihood-based information considerably improves the quality of the verification system. Finally, owing to our two-stage normalization (at the feature and score levels), we show that our system results in more stable client-score distributions across databases and in a better separation between the distributions of client and impostor scores.

In this paper, we present the BioSecure Network of Excellence and its objectives in terms of biometric evaluation. A particular focus is given in this project to multimodal evaluation, which requires special attention due to the lack of large-size available multimodal databases. We show in this paper that the evaluation of score fusion methods for two a priori independent modalities is possible on standard size (roughly 100 persons) virtual databases, but at the price of a careful statistical protocol

This letter describes an original CMOS imaging system dedicated to human face recognition. The main interest of this work is to provide ambient light invariant images and facilitate segmentation of the face from the background. This system has been implemented in a specially designed CMOS smart image sensor with only one analog memory per pixel. This simple pixel design gives the possibility to incorporate this functionality into classic low-cost CMOS image sensors. One of its possible applications is face recognition, since the human face appearance is dramatically dependent on illumination conditions. A first indoor experience with different illumination conditions shows that a simple correlation-based verification algorithm on face images of 25 people of the INT database gives promising results.

We propose an original model for automatic reading of mathematical texts, founded on a recursive approach and a hierarchical view of the latter. This hierarchy in the text has been introduced by means of the notion of mathematical object, that we characterise by a set of definitions. A prior segmentation phase of mathematical objects in text is performed, using character matching techniques and propagating the labelling process of mathematical objects around "special characters" initially recognised. Complex and dynamical tree representations are proposed for mathematical expressions detected in the text, and a dynamical set of priority rules has been defined that guides this automatic process. Finally the recognition of the elementary objects obtained permits to achieve the encoding of a mathematical expression.

We present the extension of a neural predictive system primitively designed for on-line character recognition to words. Feature extraction is performed after resampling the pen trajectory information, recorded by a digitizing tablet. Each word is modeled by the natural concatenation of letter-models corresponding to the letters composing it. Successive parts of a word trajectory are this way modeled by different neural networks and only transitions from each one to itself or to its right neighbors are permitted. A holistic and dynamical segmentation allows one to adjust letter-models to the great variability of handwriting encountered in the words. Our system combines multilayer neural networks and dynamic programming with an underlying left-right hidden Markov model (HMM). Training was performed on 7000 words from 9 writers, leading to good results in the letter-labelling process, without using any language model.

We have introduced previously (1996) a neural predictive system for on-line word recognition. Our approach implements a hidden Markov model (HMM)-based cooperation of several neural networks. The task of the HMM is to guide the training procedure of neural networks on successive parts of a word. Each word is modeled by the concatenation of letter-models corresponding to the letters composing it. In this article, we present the discriminative training procedures introduced in order to improve the results of our first model. Discriminative training is described at the local level, that is of each extracted parameter vector, and at the global level, that is the level of sequences of labels. We relate this type of training in both cases to the maximum mutual information formalism. Discriminative training was performed on 7000 words from 9 writers, leading to improved results at the character level. Moreover, the use of a neural lexical post-processor (NLPP) gives very good word recognition rates.

We present a neural prediction system for on-line writer-independent character recognition as a first step towards a word recognition system. The input feature vectors contain the pen trajectory information, recorded by a digitizing tablet. Each letter is modeled by a variable number of predictive neural networks, depending on its length. Successive parts of a letter are modeled by different multilayer neural networks, only transitions from each one to itself or to its right neighbors being permitted. To deal with the great variability of cursive handwriting, we introduce a holistic approach for both learning and recognition, combining neural networks and dynamic programming techniques. Our system is able to recognize strongly distorted and truncated letters, obtained by automatic segmentation of 10000 words from 10 different writers. Even on such databases, inappropriate to character recognition (letters in it were not recorded as handwritten isolated characters), quite good recognition rates are obtained.

The authors present a neural predictive system for on-line writer-independent character recognition. The data collection of each letter contains the pen trajectory information recorded by a digitizing tablet. Each letter is modeled by a fixed number of predictive neural networks (NN), so that a different multilayer NN models successive parts of a letter. The topology of each letter-model only permits transitions from each NN to itself or to its neighbors. In order to deal with the great variability proper to cursive handwriting in the omni-scriptor framework, they implement a holistic approach during both learning and recognition by performing adaptive segmentation. Also, the recognition step implements interactive recognition and segmentation. The approach compares neural techniques combined with dynamic programming to its extension to the hidden Markov model (HMM) framework. The first system gives quite good recognition rates on letter databases obtained from 10 different writers, and results improve considerably when one considers the extension of the first system to the durational HMM framework.