Evaluación de algoritmos de detección de complejos QRS mediante las curvas de funcionamiento ROC, DET y EPC

Se presenta una metodología para la selección de modelos utilizados en detección de eventos, empleando las curvas de funcionamiento característica de operación del receptor (ROC - Receiver Operating Characteristic), compensación del error de detección (DET - Detection Error Trade-off) y curvas de desempeño esperado (EPC - Expected Performance Curve), las cuales asumen un criterio de mínimo error para evaluar modelos. Las curvas se evalúan sobre algoritmos de detección de complejos QRS en electrocardiografía utilizando la base de datos de arritmias del MIT [8]. Los resultados obtenidos muestran que la mejor curva para representar el comportamiento de los métodos de detección es la curva EPC debido a que utiliza pruebas sobre conjuntos de entrenamiento y validación. Igualmente se obtiene que el mejor detector de complejos QRS es el basado en la amplitud y la primera derivada AF3.A methodology to select models used in detection is shown; it uses the performance curves named ROC, DET and EPC. These curves employ a criterion to evaluate the model based in obtaining a minimum error. Curves are applied over QRS complex detection algorithms using MIT Arrhythmia Database. Results show that the best curve for representing the behavior of the detection algorithms is the EPC curve, due to it uses training and test set. Equally, we obtained that the best QRS complex detector is AF3

Performance Generalization in Biometric Authentication Using Joint User-Specific and Sample Bootstraps

Biometric authentication performance is often depicted by a DET curve. We show that this curve is dependent on the choice of samples available, the demographic composition and the number of users specific to a database. We propose a two-step bootstrap procedure to take into account of the three mentioned sources of variability. This is an extension to the Bolle \etal's bootstrap subset technique. Preliminary experiments on the NIST2005 and XM2VTS benchmark databases is encouraging, e.g., the average result across all 24 systems evaluated on NIST2005 indicates that one can predict, with more than 75\% of DET coverage, an unseen DET curve with 8 times more users. Furthermore, our finding suggests that with more data available, the confidence intervals become smaller and hence more useful

Investigating the Impact of Demographic Factors on Contactless Fingerprint Interoperability

Improvements in contactless fingerprinting have resulted in contactless fingerprints becoming a faster and more convenient alternative to contact fingerprints. The interoperability between contactless fingerprints and contact fingerprints and how demographic factors can change interoperability has been challenging since COVID-19; the need for hygienic alternatives has only grown because of the sudden focus during the pandemic. Past work has shown issues with the interoperability of contactless prints from kiosk devices and phone fingerprint collection apps. Demographic bias in photography for facial recognition could affect photographed fingerprints. The paper focuses on evaluating match performance between contact and contactless fingerprints and evaluating match score bias based on five skin demographics; melanin, erythema, and the three measurements of the CIELab color space. The interoperability of three fingerprint matchers was tested. The best and worst Area Under the Curve (AUC) and Equal Error Rate (EER) values for the best-performing matcher were an AUC of 0.99398 and 0.97873 and an EER of 0.03016 and 0.07555, respectively, while the best contactless AUC and EER were 0.99337 and 0.03387 indicating that contactless match performance can be as good as contact fingerprints depending on the device. In contrast, the best and worst AUC and EER for the cellphone contactless fingerprints were an AUC of 0.96812 and 0.85772 and an EER of 0.08699 and 0.22130, falling short of the lowest performing contact fingerprints. Demographic analysis was on the top two of the three matchers based on the top one percent of non-match scores. Resulting efforts found matcher-specific bias for melanin showing specific ranges affected by low and high melanin values. While higher levels of erythema and general redness of the skin improved performance. Higher lightness values showed a decreased performance in the top-performing matcher

The effects of scarring on face recognition

The focus of this research is the effects of scarring on face recognition. Face recognition is a common biometric modality implemented for access control operations such as customs and borders. The recent report from the Special Group on Issues Affecting Facial Recognition and Best Practices for their Mitigation highlighted scarring as one of the emerging challenges. The significance of this problem extends to the ISO/IEC and national agencies are researching to enhance their intelligence capabilities. Data was collected on face images with and without scars, using theatrical special effects to simulate scarring on the face and also from subjects that have developed scarring within their lifetime. A total of 60 subjects participated in this data collection, 30 without scarring of any kind and 30 with preexisting scars. Controlled data on scarring is problematic for face recognition research as scarring has various manifestations among individuals, yet is universal in that all individuals will manifest some degree of scarring. Effect analysis was done with controlled scarring to observe the factor alone, and wild scarring that is encountered during operations for realistic contextualization. Two environments were included in this study, a controlled studio that represented an ideal face capture setting and a mock border control booth simulating an operational use case

A Software Framework For Task Based Performance Evaluation

It is difficult to objectively measure performance of complex tasks such as a surgical operation and surgical simulators require the ability to evaluate performance whether to predict surgical outcome, determine competence, provide learning feedback, etc. With no standard software framework for collecting, analyzing and evaluating performance data for complex tasks in simulations, it is investigated whether a solution can be implemented that allows for custom data collection schemes, all while being general enough to be used across many simulation platforms and can be used in a simple simulator.It is also investigated whether the implemented framework can perform its functionality while leaving a small performance footprint on the simulator. Hierarchical task analysis is investigated as a means to decompose complex tasks into their simpler sub-tasks, where data can be collected for each task and evaluated.The framework is based on hierarchical task representation to allow robust performance data of a complex task to be collected and evaluated for any type of application.A client application is developed and allows for the generation of custom scenario parameters for the task, robust performance data collection and the ability to playback previous performances for evaluation purposes.It is shown that the implemented framework has a small peformance footprint and does not affect the performance of the simulator that is using the framework for performance data collection and evaluation

Jedna klasa sistema za generisanje I distribuciju kriptoloških ključeva zasnovana na više biometrijskih modaliteta

U doktorskoj disertaciji predložena je jedna klasa sistema za generisanje i distribuciju kriptoloških ključeva na osnovu više biometrijskih modaliteta. Teorijskim okvirom obuhvaćena su dva biometrijska modaliteta, otisak prsta i iris. U radnim režimima sistem koristi heš vrednosti koje su dobijene u početnoj fazi, tj. fazi upisa. Osim ovih vrednosti, u upotrebi su pomoćni podaci koji čine da biometrijski podaci budu poništivi i na ovaj način štite privatnost biometrijskih podataka. Takođe, pružaju zaštitu kriptološkim parametrima. Biometrija irisa, kao izvor najbogatiji sa bioinformacijom, upotrebljena je u svrhu ekstrakcije tajnog ključa, dok se za servis autentifikacije koristi otisak prsta. Eksperimentalna analiza koja je obuhvatila kompletan istraživački deo rada u tezi, zasniva se na uzorcima dobijenim iz CASIA biometrijske baze podataka, a postignuti eksperimentalni rezultati ukazuju na značajne doprinose koji u velikoj meri podižu performanse ove klase sistema

Multi-system Biometric Authentication: Optimal Fusion and User-Specific Information

Verifying a person's identity claim by combining multiple biometric systems (fusion) is a promising solution to identity theft and automatic access control. This thesis contributes to the state-of-the-art of multimodal biometric fusion by improving the understanding of fusion and by enhancing fusion performance using information specific to a user. One problem to deal with at the score level fusion is to combine system outputs of different types. Two statistically sound representations of scores are probability and log-likelihood ratio (LLR). While they are equivalent in theory, LLR is much more useful in practice because its distribution can be approximated by a Gaussian distribution, which makes it useful to analyze the problem of fusion. Furthermore, its score statistics (mean and covariance) conditioned on the claimed user identity can be better exploited. Our first contribution is to estimate the fusion performance given the class-conditional score statistics and given a particular fusion operator/classifier. Thanks to the score statistics, we can predict fusion performance with reasonable accuracy, identify conditions which favor a particular fusion operator, study the joint phenomenon of combining system outputs with different degrees of strength and correlation and possibly correct the adverse effect of bias (due to the score-level mismatch between training and test sets) on fusion. While in practice the class-conditional Gaussian assumption is not always true, the estimated performance is found to be acceptable. Our second contribution is to exploit the user-specific prior knowledge by limiting the class-conditional Gaussian assumption to each user. We exploit this hypothesis in two strategies. In the first strategy, we combine a user-specific fusion classifier with a user-independent fusion classifier by means of two LLR scores, which are then weighted to obtain a single output. We show that combining both user-specific and user-independent LLR outputs always results in improved performance than using the better of the two. In the second strategy, we propose a statistic called the user-specific F-ratio, which measures the discriminative power of a given user based on the Gaussian assumption. Although similar class separability measures exist, e.g., the Fisher-ratio for a two-class problem and the d-prime statistic, F-ratio is more suitable because it is related to Equal Error Rate in a closed form. F-ratio is used in the following applications: a user-specific score normalization procedure, a user-specific criterion to rank users and a user-specific fusion operator that selectively considers a subset of systems for fusion. The resultant fusion operator leads to a statistically significantly increased performance with respect to the state-of-the-art fusion approaches. Even though the applications are different, the proposed methods share the following common advantages. Firstly, they are robust to deviation from the Gaussian assumption. Secondly, they are robust to few training data samples thanks to Bayesian adaptation. Finally, they consider both the client and impostor information simultaneously

Calculation of a composite DET curve

Abstract. The verification performance of biometric systems is normally evaluated using the receiver operating characteristic (ROC) or detection error trade-off (DET) curve. We propose two new ideas for statistical evaluation of biometric systems based on these data. The first is a new way to normalize match score distributions. A normalized match score, ˆt, is calculated as a function of the angle from a representation of (FMR, FNMR) values in polar coordinates from some center. This has the advantage that it does not produce counterintuitive results for systems with unusual DET performance. Secondly, building on this normalization we develop a methodology to calculate an average DET curve. Each biometric system is represented in terms of ˆt to allow genuine and impostor distributions to be combined, and an average DET is then calulated from these new distributions. We then show that this method is equivalent to direct averaging of DET data along each angle from the center. This procedure is then applied to data from a study of human matchers of facial images.