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

Fusion of fingerprint presentation attacks detection and matching: a real approach from the LivDet perspective

The liveness detection ability is explicitly required for current personal verification systems in many security applications. As a matter of fact, the project of any biometric verification system cannot ignore the vulnerability to spoofing or presentation attacks (PAs), which must be addressed by effective countermeasures from the beginning of the design process. However, despite significant improvements, especially by adopting deep learning approaches to fingerprint Presentation Attack Detectors (PADs), current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modelling the cause-effect relationships when two systems (spoof detection and matching) with non-zero error rates are integrated. To solve this lack of investigations in the literature, we present in this PhD thesis a novel performance simulation model based on the probabilistic relationships between the Receiver Operating Characteristics (ROC) of the two systems when implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms’ ROCs submitted to the editions of LivDet 2017-2019, the NIST Bozorth3, and the top-level VeriFinger 12.0 matchers. With the help of this simulator, the overall system performance can be predicted before actual implementation, thus simplifying the process of setting the best trade-off among error rates. In the second part of this thesis, we exploit this model to define a practical evaluation criterion to assess whether operational points of the PAD exist that do not alter the expected or previous performance given by the verification system alone. Experimental simulations coupled with the theoretical expectations confirm that this trade-off allows a complete view of the sequential embedding potentials worthy of being extended to other integration approaches

Fingerprint recognition with embedded presentation attacks detection: are we ready?

The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack detection algorithms (PAD) into such systems. Companies and institutions need to know whether such integration would make the system more “secure” and whether the technology available is ready, and, if so, at what operational working conditions. Despite significant improvements, especially by adopting deep learning approaches to fingerprint PAD, current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modeling the cause-effect relationships when two non-zero error-free systems work together. Accordingly, this paper explores the fusion of PAD into verification systems by proposing a novel investigation instrument: a performance simulator based on the probabilistic modeling of the relationships among the Receiver Operating Characteristics (ROC) of the two individual systems when PAD and verification stages are implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms’ ROCs submitted to the most recent editions of LivDet (2017-2019), the state-of-the-art NIST Bozorth3, and the top-level Veryfinger 12 matchers. Reported experiments explore significant scenarios to get the conditions under which fingerprint matching with embedded PAD can improve, rather than degrade, the overall personal verification performance

Performance Evaluation of Face Recognition Algorithms

Biometric - based techniques have emerged for recognizing individuals instead of using passwords, PINs, smart cards, plastic cards, tokens etc fo r authenticating people . Automated face recognition has become a major field of interest. In this field several facial recognition algorithms have been explored in the past few decades . A face recognition system is expected to identify faces present in images and videos automatically. The input to the facial recognition system is a two dimensional image, while the system distinguishes the input image as a users face from a pre - determined library of faces. Finally, the output is a discerned face image. This paper deals wi th the comparison of two popular dimensionality reduction algorithms such as PCA and LDA. Here, our main goal is to evaluate the performance of Principal Component Analysis and Linear Discriminant Analysis for large training data set. Finally, we concluded that LDA outperforms PCA for the large samples of training set

Identification of persons via voice imprint

Tato práce se zabývá textově závislým rozpoznáváním řečníků v systémech, kde existuje pouze omezené množství trénovacích vzorků. Pro účel rozpoznávání je navržen otisk hlasu založený na různých příznacích (např. MFCC, PLP, ACW atd.). Na začátku práce je zmíněn způsob vytváření řečového signálu. Některé charakteristiky řeči, důležité pro rozpoznávání řečníků, jsou rovněž zmíněny. Další část práce se zabývá analýzou řečového signálu. Je zde zmíněno předzpracování a také metody extrakce příznaků. Následující část popisuje proces rozpoznávání řečníků a zmiňuje způsoby ohodnocení používaných metod: identifikace a verifikace řečníků. Poslední teoreticky založená část práce se zabývá klasifikátory vhodnými pro textově závislé rozpoznávání. Jsou zmíněny klasifikátory založené na zlomkových vzdálenostech, dynamickém borcení časové osy, vyrovnávání rozptylu a vektorové kvantizaci. Tato práce pokračuje návrhem a realizací systému, který hodnotí všechny zmíněné klasifikátory pro otisk hlasu založený na různých příznacích.This work deals with the text-dependent speaker recognition in systems, where just a few training samples exist. For the purpose of this recognition, the voice imprint based on different features (e.g. MFCC, PLP, ACW etc.) is proposed. At the beginning, there is described the way, how the speech signal is produced. Some speech characteristics important for speaker recognition are also mentioned. The next part of work deals with the speech signal analysis. There is mentioned the preprocessing and also the feature extraction methods. The following part describes the process of speaker recognition and mentions the evaluation of the used methods: speaker identification and verification. Last theoretically based part of work deals with the classifiers which are suitable for the text-dependent recognition. The classifiers based on fractional distances, dynamic time warping, dispersion matching and vector quantization are mentioned. This work continues by design and realization of system, which evaluates all described classifiers for voice imprint based on different features.

Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims

Feature selection in a low cost signature recognition system based on normalized signatures and fractional distances

Producción CientíficaIn a previous work a new proposal for an efficient on-line signature recognition system with very low computational load and storage requirements was presented. This proposal is based on the use of size normalized signatures, which allows for similarity estimation, usually based on DTW or HMMs, to be performed by an easy distance calcultaion between vectors, which is computed using fractional distance. Here, a method to select representative features from the normalized signatures is presented. Only the most stable features in the training set are used for distance estimation. This supposes a larger reduction in system requirements, while the system performance is increased. The verification task has been carried out. The results achieved are about 30% and 20% better with skilled and random forgeries, respectively, than those achieved with a DTW-based system, with storage requirements between 15 and 142 times lesser and a processing speed between 274 and 926 times greater. The security of the system is also enhanced as only the representative features need to be stored, it being impossible to recover the original signature from these.Junta de Castilla y Leon (project VA077A08