André Lourenço

Unveiling the biometric potential of finger-based ECG signals

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.

Finger ECG signal for user authentication: Usability and performance

Over the past few years, the evaluation of Electrocardio-graphic (ECG) signals as a prospective biometric modality has revealed promising results. Given the vital and continuous nature of this information source, ECG signals offer several advantages to the field of biometrics; yet, several challenges currently prevent the ECG from being adopted as a biometric modality in operational settings. These arise partially due to ECG signals clinical tradition and intru-siveness, but also from the lack of evidence on the permanence of the ECG templates over time. The problem of in-trusiveness has been recently overcome with the “off-the-person” approach for capturing ECG signals. In this paper we provide an evaluation of the permanence of ECG signals collected at the fingers, with respect to the biometric authentication performance. Our experimental results on a small dataset suggest that further research is necessary to account for and understand sources of variability found in some subjects. Despite these limitations, “off-the-person” ECG appears to be a viable trait for multi-biometric or standalone biometrics, low user throughput, real-world scenarios.

Cluster Ensemble Methods: from Single Clusterings to Combined Solutions

Cluster ensemble methods attempt to find better and more robust clustering solutions by fusing information from several data partitionings. In this chapter, we address the different phases of this recent approach: from the generation of the partitions, the clustering ensemble, to the combination and validation of the combined result. While giving an overall revision of the state-of-the-art in the area, we focus on our own work on the subject. In particular, the Evidence Accumulation Clustering (EAC) paradigm is detailed and analyzed. For the validation/selection of the final partition, we focus on metrics that can quantitatively measure the consistency between partitions and combined results, and thus enabling the choice of best results without the use of additional information. Information-theoretic measures in conjunction with a variance analysis using bootstrapping are detailed and empirically evaluated. Experimental results throughout the paper illustrate the various concepts and methods addressed, using synthetic and real data and involving both vectorial and string-based data representations. We show that the clustering ensemble approach can be used in very distinct contexts with the state of the art quality results.

Study and evaluation of a single differential sensor design based on electro-textile electrodes for ECG biometrics applications

In this paper we present a study and evaluation of a custom single differential sensor design for ECG data acquisition, recurring to electro-textile electrodes as the interface between the sensor and the skin. Our work is focused on improving current signal acquisition methods for ECG biometrics, targeting wearable, continuous and unobtrusive applications. A circuit with virtual ground was also devised for enhanced usability. The purpose is to build upon and further extend the state-of-the-art in the field, improving existing signal acquisition conditions by: minimizing the number of electrical contact points with the subjects body; eliminating the need of gel in the interface with the skin; and devising a non-intrusive design that can be easily integrated into wearable devices. Experimental analysis has been performed to compare the proposed approach with a reference acquisition sensor, and results validate the potential of our method.

Probabilistic consensus clustering using evidence accumulation

Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into a pairwise co-association matrix, thus avoiding the label correspondence problem, which is intrinsic to other clustering ensemble schemes. In this paper, we propose a consensus clustering approach based on the EAC paradigm, which is not limited to crisp partitions and fully exploits the nature of the co-association matrix. Our solution determines probabilistic assignments of data points to clusters by minimizing a Bregman divergence between the observed co-association frequencies and the corresponding co-occurrence probabilities expressed as functions of the unknown assignments. We additionally propose an optimization algorithm to find a solution under any double-convex Bregman divergence. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.

Multimodal biosignal sensor data handling for emotion recognition

We present an experimental setup, sensor data handling, and evaluation framework for emotion recognition, based on multimodal biosignal sensor data. For labeled data acquisition we developed an emotion elicitation block, with a bank of labeled videos containing different triggering stimuli. A biosignal acquisition apparatus was used to collect multimodal data, namely: Electromyography (EMG); Electrocardiography (ECG); Electrodermal Activity (EDA); Blood Volume Pulse (BVP); Peripheral Temperature (SKT); and Respiration (RESP). An automated biosignal processing and feature extraction toolbox was developed to convert raw data into meaningful parameters. Experimental results revealed trends associated with triggering events, providing a baseline for emotion recognition. Through LOOCV with a k-NN classifier, we obtained recognition rates of 81% to distinguish between positive and negative emotions, and of 70% to distinguish between positive, neutral, and negative emotions.

ECG-based biometrics: A real time classification approach

Behavioral biometrics is one of the areas with growing interest within the biosignal research community. A recent trend in the field is ECG-based biometrics, where electrocardiographic (ECG) signals are used as input to the biometric system. Previous work has shown this to be a promising trait, with the potential to serve as a good complement to other existing, and already more established modalities, due to its intrinsic characteristics. In this paper, we propose a system for ECG biometrics centered on signals acquired at the subjects hand. Our work is based on a previously developed custom, non-intrusive sensing apparatus for data acquisition at the hands, and involved the pre-processing of the ECG signals, and evaluation of two classification approaches targeted at real-time or near real-time applications. Preliminary results show that this system leads to competitive results both for authentication and identification, and further validate the potential of ECG signals as a complementary modality in the toolbox of the biometric system designer.

BIT: Biosignal Igniter Toolkit.

The study of biosignals has had a transforming role in multiple aspects of our society, which go well beyond the health sciences domains to which they were traditionally associated with. While biomedical engineering is a classical discipline where the topic is amply covered, today biosignals are a matter of interest for students, researchers and hobbyists in areas including computer science, informatics, electrical engineering, among others. Regardless of the context, the use of biosignals in experimental activities and practical projects is heavily bounded by the cost, and limited access to adequate support materials. In this paper we present an accessible, albeit versatile toolkit, composed of low-cost hardware and software, which was created to reinforce the engagement of different people in the field of biosignals. The hardware consists of a modular wireless biosignal acquisition system that can be used to support classroom activities, interface with other devices, or perform rapid prototyping of end-user applications. The software comprehends a set of programming APIs, a biosignal processing toolbox, and a framework for real time data acquisition and postprocessing.

Outlier Detection in Non-intrusive ECG Biometric System

A recent trend in the field of biometrics is the use of Electrocardiographic (ECG) signals. One of the main challenges of this new paradigm is the development of non-intrusive and highly usable setups. Fingers and hand palms for example, allow the ECG acquisition at much more convenient locations than the chest, commonly used for clinical scenarios. These new locations lead to an ECG signal with lower signal to noise ratio, and more prone to noise artifacts. In this paper, we propose an outlier removal system to eliminate noisy segments, and enhance the performance of non-intrusive ECG biometric systems. Preliminary results show that this system leads to an improvement on the recognition rates, helping to further validate the potential of ECG signals as complementary biometric modality.

Check Your Biosignals Here: A new dataset for off-the-person ECG biometrics

The Check Your Biosignals Here initiative (CYBHi) was developed as a way of creating a dataset and consistently repeatable acquisition framework, to further extend research in electrocardiographic (ECG) biometrics. In particular, our work targets the novel trend towards off-the-person data acquisition, which opens a broad new set of challenges and opportunities both for research and industry. While datasets with ECG signals collected using medical grade equipment at the chest can be easily found, for off-the-person ECG data the solution is generally for each team to collect their own corpus at considerable expense of resources. In this paper we describe the context, experimental considerations, methods, and preliminary findings of two public datasets created by our team, one for short-term and another for long-term assessment, with ECG data collected at the hand palms and fingers.