A New Approach to Automatic Signature Complexity Assessment

Understanding signature complexity has been shown to be a crucial facet for both forensic and biometric appbcations. The signature complexity can be defined as the difficulty that forgers have when imitating the dynamics (constructional aspects) of other users signatures. Knowledge of complexity along with others facets such stability and signature length can lead to more robust and secure automatic signature verification systems. The work presented in this paper investigates the creation of a novel mathematical model for the automatic assessment of the signature complexity, analysing a wider set of dynamic signature features and also incorporating a new layer of detail, investigating the complexity of individual signature strokes. To demonstrate the effectiveness of the model this work will attempt to reproduce the signature complexity assessment made by experienced FDEs on a dataset of 150 signature samples

Interaction evaluation of a mobile voice authentication system

Biometric recognition is nowadays widely used in smartphones, making the users' authentication easier and more transparent than PIN codes or patterns. Starting from this idea, the EU project PIDaaS aims to create a secure authentication system through mobile devices based on voice and face recognition as two of the most reliable and user-accepted modalities. This work introduces the project and the first PIDaaS usability evaluation carried out by means of the well-known HBSI model In this experiment, participants interact with a mobile device using the PIDaaS system under laboratory conditions: video recorded and assisted by an operator. Our findings suggest variability among sessions in terms of usability and feed the next PIDaaS HCI design

Voice and face interaction evaluation of a mobile authentication platform

Biometric authentication in mobile devices has become a key aspect of application security. However, the use of dedicated sensors such as fingerprint/iris sensors may not always be feasible. As an alternative, the use of face and voice biometrics using the generic sensors integrated in smartphones is gaining momentum. This work applied the HBSI framework to analyise the user’s interaction with the mobile PIDaaS platform that integrates voice and face authentication. Our analysis enables a thorough comparison between the user’s interaction for these two modalities with the same population

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications

Biometric Systems Interaction Assessment: The State of the Art

The design and implementation of effective and efficient biometric systems presents a series of challenges to information technology (IT) designers to ensure robust performance. One of the most important factors across biometric systems, aside from algorithmic matching ability, is the human interaction influence on performance. Changes in biometric system paradigms have motivated further testing methods, especially within mobile environments, where the interaction with the device has fewer environmental constraints, whichmay severely affect system performance. Testing methods involve the need for reflecting on the influence of user-system interaction on the overall system performance in order to provide information for design and testing. This paper reflects on the state of the art of biometric systems interaction assessment, leading to a comprehensive document of the relevant research and standards in this area. Furthermore, the current challenges are discussed and thus we provide a roadmap for the future of biometrics systems interaction research

Predicting sex as a soft-biometrics from device interaction swipe gestures

Touch and multi-touch gestures are becoming the most common way to interact with technology such as smart phones, tablets and other mobile devices. The latest touch-screen input capacities have tremendously increased the quantity and quality of available gesture data, which has led to the exploration of its use in multiple disciplines from psychology to biometrics. Following research studies undertaken in similar modalities such as keystroke and mouse usage biometrics, the present work proposes the use of swipe gesture data for the prediction of soft-biometrics, specifically the user's sex. This paper details the software and protocol used for the data collection, the feature set extracted and subsequent machine learning analysis. Within this analysis, the BestFirst feature selection technique and classification algorithms (naïve Bayes, logistic regression, support vector machine and decision tree) have been tested. The results of this exploratory analysis have confirmed the possibility of sex prediction from the swipe gesture data, obtaining an encouraging 78% accuracy rate using swipe gesture data from two different directions. These results will hopefully encourage further research in this area, where the prediction of soft-biometrics traits from swipe gesture data can play an important role in enhancing the authentication processes based on touch-screen devices

MacroH2A1.1 regulates mitochondrial respiration by limiting nuclear NAD+ consumption

Histone variants are structural components of eukaryotic chromatin that can replace replication-coupled histones in the nucleosome. The histone variant macroH2A.1.1 contains a macrodomain able to bind NAD+ derived metabolites. Here, we report that macroH2A.1.1 is rapidly induced during myogenic differentiation through a switch in alternative splicing. Importantly, myotubes lacking macroH2A.1.1 display a defect in mitochondrial respiratory capacity. We find that the metabolite-interacting macrodomain is essential for sustaining optimal mitochondrial function, but dispensable for gene regulation. Through direct binding, macroH2A.1.1 inhibits basal poly-ADP ribose polymerase 1 activity and thus reduces nuclear NAD+ consumption. Consequentially, accumulation of the NAD+ precursor NMN allows the maintenance of mitochondrial NAD+ pools critical for respiration. Our data indicate that macroH2A.1.1-containing chromatin regulates mitochondrial respiration by limiting nuclear NAD+ consumption and establishing a buffer of NAD+ precursors in differentiated cells

Anestesia epidural obstétrica: actualización

Background: Childbirth is a great process that generates terrible pain, which can cause some complications, due to this, several alternatives have been innovated to reduce or control it, which have been used for years during childbirth. Methodology: A narrative review was carried out through various databases from 2018 to 2021; The search and selection of articles was carried out in journals indexed in English and Spanish. The following keywords were used: anesthesia, epidural, obstetric, update. Results: The objective of these techniques is pain relief at the time of labor, for which components have been implemented in epidural analgesia as obstetric care produced by either neuraxial, systematic or continuous analgesia, among others. We review the latest evidence on its effectiveness and safety. Conclusion: Pharmacological epidural analgesia continues to be the best option to relieve severe pain during childbirth, thanks to new updates and constant studies, these currently guarantee greater safety for both the mother and the babyAntecedentes El parto es un gran proceso que genera un terrible dolor, que puede generar algunas complicaciones, debido a esto, se han innovado varias alternativas para la disminución o el control de este, los cuales son utilizados desde hace años durante el parto. Metodología: Se realizó una revisión narrativa a través de diversas bases de datos desde el año 2018 al año 2021; la búsqueda y selección de artículos fue llevada a cabo en revistas indexadas en idioma inglés y español. Se utilizaron como palabras clave: anestesia, epidural, obstétrica, actualización. Resultados: El objetivo de estas técnicas es el alivio del dolor al momento del trabajo de parto, por lo cual se han implementado componentes en la analgesia epidural como cuidado obstétrico producido por analgesia ya sea neuroaxial, sistemática o continua, entre otras. Revisamos la evidencia más reciente sobre su efectividad y seguridad. Conclusión: La analgesia epidural farmacológica sigue siendo la mejor opción para aliviar los fuertes dolores durante el parto, gracias a las nuevas actualizaciones y a los estudios contantes estas garantizan mayor seguridad actualmente tanto para la madre como para él bebe

Exploring the relationship between stride, stature and hand size for forensic assessment

Forensic evidence often relies on a combination of accurately recorded measurements, estimated measurements from landmark data such as a subject's stature given a known measurement within an image, and inferred data. In this study a novel dataset is used to explore linkages between hand measurements, stature, leg length and stride. These three measurements replicate the type of evidence found in surveillance videos with stride being extracted from an automated gait analysis system. Through correlations and regression modelling, it is possible to generate accurate predictions of stature from hand size, leg length and stride length (and vice versa), and to predict leg and stride length from hand size with, or without, stature as an intermediary variable. The study also shows improved accuracy when a subject's sex is known a-priori. Our method and models indicate the possibility of calculating or checking relationships between a suspect's physical measurements, particularly when only one component is captured as an accurately recorded measurement