Distinguishing neurodegenerative diseases in an effective and efficient way is an increasingly important challenge. This study investigates the use of Dynamic Time Warping (DTW) as a functional connectivity (FC) measure, to distinguish patients with Subjective Cognitive Impairments (SCI), Mild Cognitive Impairments (MCI), Alzheimer’s Disease (AD) and Vascular Dementia (VaD) with EEG. The FC analysis is performed per cortical region, defining three topological ranges: short-, mid- and long-ranges. We analyze the FC at different threshold values, corresponding to the percentage of the highest values to be kept. The study highlights different FC topologies in the four conditions. Results show that DTW allows a significant distinction of VaD from SCI, MCI and AD, particularly for the short-range FC. Also, SCI, AD and VaD are significantly distinct when considering the mid-range FC for high threshold values. MCI patients show an intermediate behaviour between SCI and AD groups. Interestingly, the study also reveals that VaD group exhibits the lowest short-range connectivity and the highest long-range connectivity.

Recent years have witnessed advances of Internet of Things technologies and their applications to enable contactless sensing and elderly care in smart homes. Continuous and real-time respiration monitoring is one of the important applications to promote assistive living for elders during sleep and attracted wide attention in both academia and industry. Most of the existing respiration monitoring systems require expensive and specialized devices to sense chest displacement. However, chest displacement is not a direct indicator of breathing and thus false detection may often occur. In this paper, we design and implement a real-time and contactless respiration monitoring system by directly sensing the exhaled airflow from breathing using ultrasound signals with off-the-shelf speaker and microphone. Exhaled airflow from breathing can be regarded as air turbulence, which scatters the sound wave and results in Doppler effect. Our system works as an acoustic radar which transmits sound wave and detects the Doppler effect caused by breathing airflow. We mathematically model the relationship between the Doppler frequency change and the direction of breathing airflow. Based on this model, we design a minimum description length-based algorithm to effectively capture the Doppler effect caused by exhaled airflow. We conduct extensive experiments with 25 participants (7 elders, 2 young kids, and 16 adults, including 11 females and 14 males) in four different rooms. The participants take four different sleep postures (lying on one's back, on right/left side, and on one's stomach) in different positions of the bed. Experiment results show that our system achieves a median error lower than 0.3 breaths/min (2%) for respiration monitoring and can accurately identify Apnea. The results also demonstrate that the system is robust to different respiration styles (shallow, normal, and deep), respiration rate variation, ambient noise, sensing distance variation (within 0.7 m), and transmitted signal frequency variation.

Most of recent methods for action/activity recognition, usually based on static classifiers, have achieved improvements by integrating context of local interest point (IP) features such as spatiotemporal IPs by characterising their neighbourhood under different scales. In this study, the authors propose a new approach that explicitly models the sequential aspect of activities. First, a sliding window segmentation technique splits the video stream into overlapping short segments. Each window is characterised by a local bag of words of IPs encoded by motion information. A first‐layer support vector machine provides for each window a vector of conditional class probabilities that summarises all discriminant information that is relevant for sequence recognition. The sequence of these stochastic vectors is then fed to a hidden conditional random field for inference at the sequence level. They also show how their approach can be naturally extended to the problem of conjoint segmentation and recognition of a sequence of action classes within a continuous video stream. They have tested their model on various human action and activity datasets and the obtained results compare favourably with current state of the art.

In this paper, we consider the problem of iris recognition in the context of video-based distant acquisition. We propose several systems aiming at improving the poor performance resulting from image degradations (low resolution, blur, and lack of texture) obtained from such acquisitions. Our approach is based on simple super-resolution techniques applied at the pixel level on the different frames of a video, improved by considering some quality criteria. Our main novelty is the introduction of a local quality measure in the fusion scheme. This measure relies on a gaussian mixture model estimation of clean iris texture distribution. It can also be used to compute a global quality measure of the normalized iris image which can be used either for the selection of the best images in a sequence or in the fusion scheme. Extensive experiments on the QFIRE database at different acquisition distances (5, 7, and 11 ft) show the big improvement brought by the use of the global quality for both scenarios. Moreover, the local quality-based fusion scheme further increases the performance due to its ability to consider locally the different parts of the image, and therefore, to discard poorly segmented pixels in the fusion.

In this paper, we propose a new approach based on watermarking for fusing biometric modalities. The main idea of the proposed approach is to use the watermark both for security purpose and as an additional information related to the person, therefore increasing the personal data used for verification. The host image of the face is watermarked in the multi-resolution space by the palmprint using a watermarking technique based on wavelet packet decomposition. For the verification stage, the characterization of the watermarked face is provided by Gabor filters while classification is performed by SVMs. The experimental results show that this technique ensures a significant performance improvement in both identity verification and biometric security over the use of a single system.

This study is part of the development of a remote home healthcare monitoring application designed to detect distress situations through several types of sensors. The multisensor fusion can provide more accurate and reliable information compared to information provided by each sensor separately. Furthermore, data from multiple heterogeneous sensors present in the remote home healthcare monitoring systems have different degrees of imperfection and trust. Among the multisensor fusion methods, Dempster-Shafer theory (DST) is currently considered the most appropriate for representing and processing the imperfect information. Based on a graphical representation of the DST called evidential networks, a structure of heterogeneous data fusion from multiple sensors for fall detection has been proposed. The evidential networks, implemented on our remote medical monitoring platform, are also proposed in this paper to maximize the performance of automatic fall detection and thus make the system more reliable. However, the presence of noise, the variability of recorded signals by the sensors, and the failing or unreliable sensors may thwart the evidential networks performance. In addition, the sensors signals nonstationary nature may degrade the experimental conditions. To compensate the nonstationary effect, the time evolution is considered by introducing the dynamic evidential network which was evaluated by the simulated fall scenarios corresponding to various use cases.

We present in this paper a multi-shot human reidentification system from video sequences based on SURF matching. Our contribution is about the matching step which is crucial. In this context, we propose a new method of SURF matching via sparse representation. Each SURF Interest Point in the test sequence is represented by a sparse representation of SURFs points in the reference dataset. For efficiency purposes, a dynamic dictionary is selected for each SURF from this dataset through KD-Tree Neighborhood search. Then a majority vote rule is applied to classify the test sequence. This approach is evaluated on two public datasets : PRID-2011 and CAVIAR4REID. The experimental results show that our approach compares favorably with and outperforms current state-of-the-art on the two datasets by 1% to 7%.

We present a novel implementation of Triplet Markov Fields (TMF) for the unsupervised region segmentation of challenging eye images, representative of the iris recognition context. Results confirm the interest of such models over the classical Hidden Markov Field (HMF) and traditional gradient-based approaches for iris and periocular detection. We show that the precision of the resulting normalization circles is largely improved through the use of such TMF model as well as the quality of the image segmentation, despite of various degradations. These results are promising for further integration of TMF approaches in iris verification systems.

Today elderly people are the fastest growing segment of the population in developed countries, and they desire to live as independently as possible. But independent lifestyles come with risks and challenges. Medical in-home telemonitoring (and, more generally, telemedicine) is a solution to deal with these challenges and to ensure that elderly people can live safely and independently in their own homes for as long as possible. In this context we propose an automatic in-home healthcare monitoring system for several uses and to meet the needs identified above. The proposed telemonitoring system is a multimodal platform with several sensors that can be installed at home and enables us to have a full and tightly controlled universe of data sets. It integrates elderly physiological and behavioral data, the acoustical environment of the elderly, environmental conditions and medical knowledge. Each modality is processed and analyzed by specific algorithms. A data fusion approach based on fuzzy logic with a set of rules directed by medical recommendations, is used to fuse the various subsystem outputs. This multimodal fusion increases the reliability of the whole system by detecting several distress situations. In fact this fusion approach takes into account temporary sensor malfunction and increases the system reliability and the robustness in the case of environmental disturbances or material limits (Battery, RF range, etc.). The Fuzzy logic fusion methods brings high flexibility to the telemonitoring platform especially in combining modalities or adding other sensors. The proposed telemonitoring system will ensure pervasive in-home health monitoring for elderly people.

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.

In this paper, a 3D Active Shape Model (3DASM) algorithm is presented to automatically locate facial landmarks from different views. The 3DASM is trained by setting different shape and texture parameters of 3D Morphable Model (3DMM). Using 3DMM to synthesize training data offers us two advantages: first, few manual operations are need, except labeling landmarks on the mean face of 3DMM. Second, since the learning data are directly from 3DMM, landmarks have one to one correspondence between the 2D points detected from the image and 3D points on 3DMM. This kind of correspondence will benefit 3D face reconstruction processing. During fitting, 3D rotation parameters are added comparing to 2D Active Shape Model (ASM). So we separate shape variations into intrinsic change (caused by the character of different person) and extrinsic change (caused by model projection). The experimental results show that our method is robust to pose variation.

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.

With the increasing use of biometrics, more and more concerns are being raised about the privacy of the personal biometric data. Conventional biometric systems store biometric templates in a database. This may lead to the possibility of tracking personal information stored in one database by getting access to another database through cross-database matching. Moreover, biometric data are permanently associated with the user. Hence if stolen, they are lost permanently and become unusable in that system and possibly in all other systems based on that biometrics. In order to overcome this non-revocability of biometrics, we propose a two factor scheme to generate cancelable iris templates using iris-biometric and password. We employ a user specific shuffling key to shuffle the iris codes. Additionally, we introduce a novel way to use error correcting codes (ECC) to reduce the variabilities in biometric data. The shuffling scheme increases the impostor Hamming distance leaving genuine Hamming distance intact while the ECC reduce the Hamming distance for genuine comparisons by a larger amount than for the impostor comparisons. This results in better separation between genuine and impostor users which improves the verification performance. The shuffling key is protected by a password which makes the system truly revocable. The biometric data is stored in a protected form which protects the privacy. The proposed scheme reduces the equal error rate (EER) of the system by more than 90% (e.g., from 1.70% to 0.057% on the NIST-ICE database).

Learning and recognizing human activities of daily living(ADL), is very useful and essential to build a pervasive home monitoring system. These monitoring technologies are indispensable for developing the next generation of smart houses. In this paper we describe a fuzzy logic system for recognizing activities in home environment using a set of sensors: physiological sensors (cardiac frequency, activity or agitation, posture and fall detection sensor), microphones, infrared sensors, debit sensors and state-change sensors. Motivated by the fact that fuzzy controllers have been successfully embedded within billions of dollars in commercial products, plus the characteristic of data providing from each sensor, the fusion of the different sensors has been performed by using fuzzy logic. This fuzzy logic approach allowed us to recognize several activities of daily living (ADLs) for ubiquitous healthcare.

Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing image quality which may be due to a change of acquisition devices and/or device capturing configurations, we observe that the top performing fusion algorithms are those that exploit automatically derived quality measurements. Our evaluation also suggests that while using all the available biometric sensors can definitely increase the fusion performance, this comes at the expense of increased cost in terms of acquisition time, computation time, the physical cost of hardware, and its maintenance cost. As demonstrated in our experiments, a promising solution which minimizes the composite cost is sequential fusion, where a fusion algorithm sequentially uses match scores until a desired confidence is reached, or until all the match scores are exhausted, before outputting the final combined score.

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 discuss the use of a dynamic packet to time-slot assignment (DPA) algorithm combined with a nonlinear interference suppression method for a TDMA-based network equipped with an antenna array in the base station. Single and multicell cell scenarios are considered. The whole receiver strategy (combined DPA and non-linear spatial processing) is then evaluated for different degrees of spatial diversity. Results show that the combined strategy produces a considerable throughput improvement, specially for a high offered load condition.

This paper investigates the cooperation of online and off-line handwriting word recognition systems. Our goal is to improve a mature online recognition system by, exploiting the complementary information present in the off-line representation built from online signal. After describing the online and off-line HMM based handwriting recognition systems, we propose a formal framework, which allows describing different strategies for combining the two HMM. These schemes are then evaluated on the UNIPEN database, both for isolated character and word recognition tasks.

This paper focuses on designing a handwriting recognition system dealing with on-line signal, i.e. temporal handwriting signal captured through an electronic pen or a digitalized tablet. We present here some new results concerning a hybrid on-line handwriting recognition system based on Hidden Markov Models (HMMs) and Neural Networks (NNs), which has already been presented in several contributions. In our approach, a letter-model is a Left-Right HMM, whose emission probability densities are approximated with mixtures of predictive multilayer perceptrons. The basic letter models are cascaded in order to build models for words and sentences. At the word level, recognition is performed thanks to a dictionary organized with a tree-structure. At the sentence level, a word-predecessor conditioned frame synchronous beam search algorithm allows to perform simultaneously segmentation into words and word recognition. It processes through the building of a word graph from which a set of candidate sentences may be extracted. Word and sentence recognition performances are evaluated on parts of the UNIPEN international database.

Hidden Markov models (HMMs) have become within a few years the main technology for online handwritten word recognition (HWR). We consider segment models which generalize HMMs, these models aim at modeling the signal at a global level rather than at the frame level and have been shown to overcome standard HMMs in their modeling ability. We propose a segment model which allows us to automatically handle different writing styles. We compare our system on the isolated character set of the UNIPEN database with a reference system and a baseline segment model.

Previously we have introduced a neural predictive system for on-line and off-line word recognition (Garcia-Salicetti et al., 1995; 1996; 1997). Words are recognized thanks to a dictionary which is used in a postprocessing stage. We focus on this lexical part of the system and more precisely on the preselection technique that is used to reduce the computational complexity. We define and compare two edit distances and show how the results can be improved through the use of the confusion matrix.

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.

This paper presents a system dedicated to the recognition of English cursive words drawn on a digitizing tablet. This system uses an analytical approach in the sense that it tries to localise the letters of the word to be recognised due to a set of letter prototypes. Its main originality is its ability to upgrade its prototype set at run-time. This is done based on a recognition module that produces an accurate letter segmentation of the words it identifies. For a given user, this adaptation ensures a very good performance after the recognition of a few hundred words. Four series of experiments demonstrate the efficiency of this new approach.

The authors present a system able to recognize English words dynamically written on a digitizing tablet. It relies on a statistical approach, in which letters are first identified with a certain likelihood. A score is then given to each word in the dictionary for the final recognition. In order to optimize the allograph identification phase, a hierarchical classification algorithm allows one to group the allographs corresponding to a given letter and a given size. Performances on a database of 5000 words and a dictionary of 1000 words are very good and the extension to multi-scriptors is possible thanks to a partial relearning.>