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Multimodal systems are a workaround to enhance the robustness and effectiveness of biometric systems. A proper multimodal dataset is of the utmost importance to build such systems. The literature presents some multimodal datasets, although, to the best of our knowledge, there are no previous studies combining face, iris/eye, and vital signals such as the Electrocardiogram (ECG). Moreover, there is no methodology to guide the construction and evaluation of a chimeric dataset. Taking that fact into account, we propose to create a chimeric dataset from three modalities in this work: ECG, eye, and face. Based on the Doddington Zoo criteria, we also propose a generic and systematic protocol imposing constraints for the creation of homogeneous chimeric individuals, which allow us to perform a fair and reproducible benchmark. Moreover, we have proposed a multimodal approach for these modalities based on state-of-the-art deep representations built by convolutional neural networks. We conduct the experiments in the open-world verification mode and on two different scenarios (intra-session and inter-session), using three modalities from two datasets: CYBHi (ECG) and FRGC (eye and face). Our multimodal approach achieves impressive decidability of 7.20 ± 0.18, yielding an almost perfect verification system (i.e., Equal Error Rate (EER) of 0.20% ± 0.06) on the intra-session scenario with unknown data. On the inter-session scenario, we achieve a decidability of 7.78 ± 0.78 and an EER of 0.06% ± 0.06. In summary, these figures represent a gain of over 28% in decidability and a reduction over 11% of the EER on the intra-session scenario for unknown data compared to the best-known unimodal approach. Besides, we achieve an improvement greater than 22% in decidability and an EER reduction over 6% in the inter-session scenario.

For multi-objective optimization algorithms, one of the key features is the ability to find a good approximation to the optimal trade-off among the multiple objectives. The most traditional algorithms focus only on the distribution of solutions in the objective space. Nevertheless, a good representation of solutions in the decision space is also important from the point of view of the decision-making process. This work presents a dominance-weighted uniformity-based multi-objective algorithm. The selection phase of the algorithm performs a greedy search of the uniformity measure weighted by a certain measure of dominance. Preliminary experiments suggest that this method promotes the uniformity of the population in the decision variable space while keeping the convergence performance in the objective space.

AbstractThe spatial scan statistic is a widely used technique for detecting spatial clusters. Several extensions of this technique have been developed over the years. The objectives of these techniques are the detection accuracy improvement and a flexibilization on the search clusters space. Based on Voronoi-Based Scan (VBScan), we propose a biobjective approach using a recursively VBScan method called multi-objective multiple clusters VBScan (MOMC-VBScan), alongside a new measure called matching. This approach aims to identify and delineate all multiple significant anomalies in a search space. We conduct several experiments on different simulated maps and two real datasets, showing promising results. The proposed approach proved to be fast and with good precision in determining the partitions.

The QRS complex is a very important part of a heartbeat in the electrocardiogram signal, and it provides useful information for physicians to diagnose heart diseases. Accurately detecting the fiducial points that compose the QRS complex is a challenging task. Another issue concerning the QRS detection is its computational costs since the algorithm should have a fast and real-time response. In this context, there is a trade-off between computational cost and precision. Convolutional networks are a deep learning approach, and it has achieved impressive results in several computer vision and pattern recognition problems. Nowadays there is hardware that fully embeds convolutional network models, significantly reducing computational cost for real-world and real-time applications. In this direction, this work proposes a deep learning approach, based on convolutional network, aiming to detect heartbeat pattern. We tested two different architectures with two different proposes, one very deep and that has small receptive fields, and the other that has larger receptive fields. Preliminary experiments on the MIT-BIH arrhythmia database showed that the studied convolutional network presents promising results for QRS detection which are comparable with state-of-the-art methods.

In this work, we propose to detect the iris and periocular regions simultaneously using coarse annotations and two well-known object detectors: YOLOv2 and Faster R-CNN. We believe coarse annotations can be used in recognition systems based on the iris and periocular regions, given the much smaller engineering effort required to manually annotate the training images. We manually made coarse annotations of the iris and periocular regions (≈122K images from the visible (VIS) spectrum and ≈38K images from the near-infrared (NIR) spectrum). The iris annotations in the NIR databases were generated semi-automatically by first applying an iris segmentation CNN and then performing a manual inspection. These annotations were made for 11 well-known public databases (3 NIR and 8 VIS) designed for the iris-based recognition problem, and are publicly available to the research community1. Experimenting our proposal on these databases, we highlight two results. First, the Faster R-CNN + Feature Pyramid Network (FPN) model reported an Intersection over Union (IoU) higher than YOLOv2 (91.86% vs 85.30%). Second, the detection of the iris and periocular regions being performed simultaneously is as accurate as performed separately, but with a lower computational cost, i.e. two tasks were carried out at the cost of one.

The iris is considered as the biometric trait with the highest unique probability. The iris location is an important task for biometrics systems, affecting directly the results obtained in specific applications such as iris recognition, spoofing and contact lenses detection, among others. This work defines the iris location problem as the delimitation of the smallest squared window that encompasses the iris region. In order to build a benchmark for iris location we annotate (iris squared bounding boxes) four databases from different biometric applications and make them publicly available to the community. Besides these 4 annotated databases, we include 2 others from the literature. We perform experiments on these six databases, five obtained with near infra-red sensors and one with visible light sensor. We compare the classical and outstanding Daugman iris location approach with two window based detectors: 1) a sliding window detector based on features from Histogram of Oriented Gradients (HOG) and a linear Support Vector Machines (SVM) classifier; 2) a deep learning based detector fine-tuned from YOLO object detector. Experimental results showed that the deep learning based detector outperforms the other ones in terms of accuracy and runtime (GPUs version) and should be chosen whenever possible.

The spatial scan statistic is widely used by public health professionals in the detection of spatial clusters in inhomogeneous point process. The most popular version of the spatial scan statistic uses a circular-shaped scanning window. Several other variants, using other parametric or non-parametric shapes, are also available. However, none of them offer information about the uncertainty on the borders of the detected clusters.

The global expansion of biometric systems promotes the emergence of new and more robust biometric modalities. In that context, electroencephalogram (EEG) based biometric interest has been growing in recent years. In this study, a novel approach for EEG representation, based on deep learning, is proposed. The method was evaluated on a database containing 109 subjects, and all 64 EEG channels were used as input to a Deep Convolution Neural Network. Data augmentation techniques are explored to train the deep network and results showed that the method is a promising path to represent brain signals, overcoming baseline methods published in the literature.

Usually, in the deep learning community, it is claimed that generalized representations that yielding outstanding performance / effectiveness require a huge amount of data for learning, which directly affect biometric applications. However, recent works combining transfer learning from other domains have surmounted such data application constraints designing interesting and promising deep learning approaches in diverse scenarios where data is not so abundant. In this direction, a biometric system for the periocular region based on deep learning approach is designed and applied on two non-cooperative ocular databases. Impressive representation discrimination is achieved with transfer learning from the facial domain (a deep convolutional network, called VGG) and fine tuning in the specific periocular region domain. With this design, our proposal surmounts previous state-of-the-art results on NICE (mean decidability of 3.47 against 2.57) and MobBio (equal error rate of 5.42% against 8.73%) competition databases.

This paper presents a hybrid PSO algorithm (Particle Swarm Optimization) with an ILS (Iterated Local Search) operator for handling equality constraints problems in mono-objective optimization problems. The ILS can be used to locally search around the best solutions in some generations, exploring the attraction basins in small portions of the feasible set. This process can compensate the difficulty of the evolutionary algorithm to generate good solutions in zero-volume regions. The greatest advantage of the operator is the simple implementation. Experiments performed on benchmark problems shows improvement in accuracy, reducing the gap for the tested problems.

There are several biometric-based systems which rely on a single biometric modality, most of them focus on face, iris or fingerprint. Despite the good accuracies obtained with single modalities, these systems are more susceptible to attacks, i.e, spoofing attacks, and noises of all kinds, especially in non-cooperative (in-the-wild) environments. Since non-cooperative environments are becoming more and more common, new approaches involving multi-modal biometrics have received more attention. One challenge in multimodal biometric systems is how to integrate the data from different modalities. Initially, we propose a deep transfer learning optimized from a model trained for face recognition achieving outstanding representation for only iris modality. Our feature level fusion by means of features selection targets the use of the Particle Swarm Optimization (PSO) for such aims. In our pool, we have the proposed iris fine-tuned representation and a periocular one from previous work of us. We compare this approach for fusion in feature level against three basic function rules for matching at score level: sum, multi, and min. Results are reported for iris and periocular region (NICE.II competition database) and also in an open-world scenario. The experiments in the NICE.II competition databases showed that our transfer learning representation for iris modality achieved a new state-of-the-art, i.e., decidability of 2.22 and 14.56% of EER. We also yielded a new state-of-the-art result when the fusion at feature level by PSO is done on periocular and iris modalities, i.e., decidability of 3.45 and 5.55% of EER.

This paper presents a hybrid PSO algorithm with a VND-based operator for handling equality constraint problems in continuous optimization. The VND operator can be defined both as a local search and a kind of elitism operator for equality constraint problems playing the role of ``fixing'' the best estimates of the feasible set. Experiments performed on benchmark problems suggest that the VND operator can enhance both the convergence speed and the accuracy of the final result.

Classifying arrhythmias can be a tough task for a human being and automating this task is highly desirable. Nevertheless fully automatic arrhythmia classification through Electrocardiogram (ECG) signals is a challenging task when the inter-patient paradigm is considered. For the inter-patient paradigm, classifiers are evaluated on signals of unknown subjects, resembling the real world scenario. In this work, we explore a novel ECG representation based on vectorcardiogram (VCG), called temporal vectorcardiogram (TVCG), along with a complex network for feature extraction. We also fine-tune the SVM classifier and perform feature selection with a particle swarm optimization (PSO) algorithm. Results for the inter-patient paradigm show that the proposed method achieves the results comparable to state-of-the-art in MIT-BIH database (53% of Positive predictive (+P) for the Supraventricular ectopic beat (S) class and 87.3% of Sensitivity (Se) for the Ventricular ectopic beat (V) class) that TVCG is a richer representation of the heartbeat and that it could be useful for problems involving the cardiac signal and pattern recognition.

The automation process of Pap smear analysis holds the potential to address women's health care in the face of an increasing population and respective collected data. A fundamental step for automating analysis is cell detection from light microscopy images. Such information serves as input to cell classification algorithms and diagnostic recommendation tools. This paper describes an approach to nuclei cell segmentation, which critically impacts the following steps for cell analyses. We developed an algorithm combining clustering and genetic algorithms to detect image regions with high diagnostic value. A major problem when performing the segmentation of images is the cellular overlay. We introduce a new nuclear targeting approach using heuristics associated with a multi-objective genetic algorithm. Our experiments show results using a public 45-image dataset, including comparison to other cell detection approaches. The findings suggest an improvement in the nuclei segmentation and promise to support more sophisticated schemes for data quality control.

For decades iris recognition has been widely studied by the scientific community due to its almost unique and stable patterns. Iris recognition biometric systems apply mathematical pattern-recognition techniques to an iris' image of an individual's eye to extract its feature vector. Comparing the dissimilarities from two feature vectors with an acceptance threshold, the system decides if the two vectors are from the same individual's eye. If applied in a well-controlled environment, iris recognition can achieve outstanding accuracies, however, to accomplish that in non-controlled environments is still a challenge researchers are constantly trying to compensate open issues in this context. In order to better explore the patterns found in the iris, researchers have recently begun using a classification approach using multiple signatures, hoping to improve the algorithm robustness. This work aims to explore the effectiveness and scalability of using multiple signatures with a 2-D Gabor filter in a biometric verification system through iris recognition. This is done using two independent Genetic Algorithms to search for the best parameters to the feature extraction technique and on the acceptance frontier search. The method was evaluated by analyzing the behavior of the Half Total Error Rate (HTER) when the number of partitions varies. The experiments showed that the best result was found with 12 partitions on the iris, reaching 0.21% of HTER.

An electrocardiogram (ECG) measures the electric activity of the heart and has been widely used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing the electrical signal of each heartbeat, i.e., the combination of action impulse waveforms produced by different specialized cardiac tissues found in the heart, it is possible to detect some of its abnormalities. In the last decades, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, we survey the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used. In addition, we describe some of the databases used for evaluation of methods indicated by a well-known standard developed by the Association for the Advancement of Medical Instrumentation (AAMI) and described in ANSI/AAMI EC57:1998/(R)2008 (ANSI/AAMI, 2008). Finally, we discuss limitations and drawbacks of the methods in the literature presenting concluding remarks and future challenges, and also we propose an evaluation process workflow to guide authors in future works.

The classification of heartbeats using electrocardiogram (ECG) aiming arrhythmia detection is a well researched subject and still there are room for improvements concerning the recommended databases. In this sense, aiming to classify heartbeats for arrhythmia detection, we extend a previous ours proposal that uses vectorcardiogram, a bi-dimensional representation of two ECG leads, by incorporating the time component producing a three-dimensional representation, the temporal vectorcardiogram. Along with the new representation, also we apply complex networks to extract features from the temporal VCG. The new proposed features feed then a Support Vector Machines (SVM) classifier. The temporal VCG have increased in the global accuracy, and have better results classifying the N and S classes, when it is compared with the best result to our previous work with VCG. We conclude that new techniques to extract 3D features from the Temporal VCG could be an interesting research direction.

Methods aiming detection and inference of spatial clusters are of great relevance. Both for its applicability to public health problems, as well as for the effective scientific interest in the development of these methods. The main techniques are based on spatial scan statistics and many applications link this statistic in efficient optimization methods. Recently, regularity functions have been proposed to control the excessive freedom of the shape of spatial clusters whenever the spatial scan statistic is used. We present a novel scan statistic algorithm employing a function based on the geographical dispersion to penalize the presence of a huge gap between the areas in candidate clusters, the dispersion function. The proposed multi-objective scan maximize the spatial scan and minimize the dispersion function, using particle swarm optimization. We show that, compared to the non-connectivity regularity function, the multi-objective scan with dispersion function is faster and suited for the detection of moderately irregularly shaped clusters. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. We show that, the multi-objective approach with the dispersion function is a promising algorithm for the detection of irregularly shaped clusters.