Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers

Visible, near infrared and thermal hand-based image biometric recognition

Biometric Recognition refers to the automatic identification of a person based on his or her anatomical characteristic or modality (i.e., fingerprint, palmprint, face) or behavioural (i.e., signature) characteristic. It is a fundamental key issue in any process concerned with security, shared resources, network transactions among many others. Arises as a fundamental problem widely known as recognition, and becomes a must step before permission is granted. It is supposed that protects key resources by only allowing those resources to be used by users that have been granted authority to use or to have access to them. Biometric systems can operate in verification mode, where the question to be solved is Am I who I claim I am? or in identification mode where the question is Who am I? Scientific community has increased its efforts in order to improve performance of biometric systems. Depending on the application many solutions go in the way of working with several modalities or combining different classification methods. Since increasing modalities require some user inconvenience many of these approaches will never reach the market. For example working with iris, face and fingerprints requires some user effort in order to help acquisition. This thesis addresses hand-based biometric system in a thorough way. The main contributions are in the direction of a new multi-spectral hand-based image database and methods for performance improvement. The main contributions are: A) The first multi-spectral hand-based image database from both hand faces: palmar and dorsal. Biometric database are a precious commodity for research, mainly when it offers something new like visual (VIS), near infrared (NIR) and thermography (TIR) images at a time. This database with a length of 100 users and 10 samples per user constitute a good starting point to check algorithms and hand suitability for recognition. B) In order to correctly deal with raw hand data, some image preprocessing steps are necessary. Three different segmentation phases are deployed to deal with VIS, NIR and TIR images specifically. Some of the tough questions to address: overexposed images, ring fingers and the cuffs, cold finger and noise image. Once image segmented, two different approaches are prepared to deal with the segmented data. These two approaches called: Holistic and Geometric define the main focus to extract the feature vector. These feature vectors can be used alone or can be combined in some way. Many questions can be stated: e.g. which approach is better for recognition?, Can fingers alone obtain better performance than the whole hand? and Is thermography hand information suitable for recognition due to its thermoregulation properties? A complete set of data ready to analyse, coming from the holistic and geometric approach have been designed and saved to test. Some innovative geometric approach related to curvature will be demonstrated. C) Finally the Biometric Dispersion Matcher (BDM) is used in order to explore how it works under different fusion schemes, as well as with different classification methods. It is the intention of this research to contrast what happen when using other methods close to BDM like Linear Discriminant Analysis (LDA). At this point, some interesting questions will be solved, e.g. by taking advantage of the finger segmentation (as five different modalities) to figure out if they can outperform what the whole hand data can teach us.El Reconeixement Biomètric fa referència a la identi cació automàtica de persones fent us d'alguna característica o modalitat anatòmica (empremta digital) o d'alguna característica de comportament (signatura). És un aspecte fonamental en qualsevol procés relacionat amb la seguretat, la compartició de recursos o les transaccions electròniques entre d'altres. És converteix en un pas imprescindible abans de concedir l'autorització. Aquesta autorització, s'entén que protegeix recursos clau, permeten així, que aquests siguin utilitzats pels usuaris que han estat autoritzats a utilitzar-los o a tenir-hi accés. Els sistemes biomètrics poden funcionar en veri cació, on es resol la pregunta: Soc jo qui dic que soc? O en identi cació on es resol la qüestió: Qui soc jo? La comunitat cientí ca ha incrementat els seus esforços per millorar el rendiment dels sistemes biomètrics. En funció de l'aplicació, diverses solucions s'adrecen a treballar amb múltiples modalitats o combinant diferents mètodes de classi cació. Donat que incrementar el número de modalitats, representa a la vegada problemes pels usuaris, moltes d'aquestes aproximacions no arriben mai al mercat. La tesis contribueix principalment en tres grans àrees, totes elles amb el denominador comú següent: Reconeixement biometric a través de les mans. i) La primera d'elles constitueix la base de qualsevol estudi, les dades. Per poder interpretar, i establir un sistema de reconeixement biomètric prou robust amb un clar enfocament a múltiples fonts d'informació, però amb el mínim esforç per part de l'usuari es construeix aquesta Base de Dades de mans multi espectral. Les bases de dades biomètriques constitueixen un recurs molt preuat per a la recerca; sobretot si ofereixen algun element nou com es el cas. Imatges de mans en diferents espectres electromagnètics: en visible (VIS), en infraroig (NIR) i en tèrmic (TIR). Amb un total de 100 usuaris, i 10 mostres per usuari, constitueix un bon punt de partida per estudiar i posar a prova sistemes multi biomètrics enfocats a les mans. ii) El segon bloc s'adreça a les dues aproximacions existents en la literatura per a tractar les dades en brut. Aquestes dues aproximacions, anomenades Holística (tracta la imatge com un tot) i Geomètrica (utilitza càlculs geomètrics) de neixen el focus alhora d'extreure el vector de característiques. Abans de tractar alguna d'aquestes dues aproximacions, però, és necessària l'aplicació de diferents tècniques de preprocessat digital de la imatge per obtenir les regions d'interès desitjades. Diferents problemes presents a les imatges s'han hagut de solucionar de forma original per a cadascuna de les tipologies de les imatges presents: VIS, NIR i TIR. VIS: imatges sobre exposades, anells, mànigues, braçalets. NIR: Ungles pintades, distorsió en forma de soroll en les imatges TIR: Dits freds La segona àrea presenta aspectes innovadors, ja que a part de segmentar la imatge de la ma, es segmenten tots i cadascun dels dits (feature-based approach). Així aconseguim contrastar la seva capacitat de reconeixement envers la ma de forma completa. Addicionalment es presenta un conjunt de procediments geomètrics amb la idea de comparar-los amb els provinents de l'extracció holística. La tercera i última àrea contrasta el procediment de classi cació anomenat Biometric Dispersion Matcher (BDM) amb diferents situacions. La primera relacionada amb l'efectivitat respecte d'altres mètode de reconeixement, com ara l'Anàlisi Lineal Discriminant (LDA) o bé mètodes com KNN o la regressió logística. Les altres situacions que s'analitzen tenen a veure amb múltiples fonts d'informació, quan s'apliquen tècniques de normalització i/o estratègies de combinació (fusió) per millorar els resultats. Els resultats obtinguts no deixen lloc per a la confusió, i són certament prometedors en el sentit que posen a la llum la importància de combinar informació complementària per obtenir rendiments superiors

A Framework for Verification in Contactless Secure Physical Access Control and Authentication Systems

Biometrics is one of the very popular techniques in user identification for accessing institutions and logging into attendance systems. Currently, some of the existing biometric techniques such as the use of fingerprints are unpopular due to COVID-19 challenges. This paper identifies the components of a framework for secure contactless access authentication. The researcher selected 50 journals from Google scholar which were used to analyze the various components used in a secure contactless access authentication framework. The methodology used for research was based on the scientific approach of research methodology that mainly includes data collection from the 50 selected journals, analysis of the data and assessment of results. The following components were identified: database, sensor camera, feature extraction methods, matching and decision algorithm. Out of the considered journals the most used is CASIA database at 40%, CCD Sensor camera with 56%, Gabor feature extraction method at 44%, Hamming distance for matching at 100% and PCA at 100% was used for decision making. These findings will assist the researcher in providing a guide on the best suitable components. Various researchers have proposed an improvement in the current security systems due to integrity and security problems

Biometrics for internet‐of‐things security: A review

The large number of Internet‐of‐Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric‐based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric‐cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state‐of‐the‐art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward‐looking issues and future research directions