Academic literature on the topic 'Multi-Biometric'

L’objectif de ces travaux de recherche est d’étudier les méthodes d’évaluation de laqualité des images biométriques multimodales sur des échantillons acquis de manièrenon contrainte. De nombreuses s études ont noté l’importance de la qualité del’échantillon pour un système de reconnaissance ou un algorithme de comparaison,puisque la performance du système biométrique est intrinsèquement dépendant dela qualité des images de l’échantillon. Dès lors, la nécessité d’évaluer la qualitédes échantillons biométriques pour plusieurs modalités (empreintes digitales, iris,visage, etc.) est devenue primordiale notamment avec l’apparition de systèmesbiométriques multimodaux de haute précision.Après une introduction présentant un historique de la biométrie et des préceptesliés à la qualité des échantillons biométriques, nous présentons le concept d’évaluationde la qualité des échantillons pour plusieurs modalités. Les normes de qualitéISO / CEI récemment établies pour les empreintes digitales, l’iris et le visage sontprésentées. De plus, des approches d’évaluation de la qualité des échantillons conçuesspécifiquement pour les empreintes digitales avec et sans contact, pour l’iris(dont une image est capturée en proche infrarouge et dans le domaine visible),ainsi que le visage sont étudiées. Finalement, des techniques d’évaluation des performancesdes mesures de qualité des échantillons biométriques sont égalementétudiées.Sur la base des conclusions formulées suite à l’étude des solutions algorithmiques portant sur l’évaluation de la qualité des échantillons biométriques, nous proposonsun cadre commun pour l’évaluation de la qualité d’image biométrique pourplusieurs modalité. Après avoir étudié les attributs de qualité basés sur l’image parmodalité biométrique, nous examinons quelle intersection existe pour l’ensembledes modalités. Ensuite, nous sélectionnons et redéfinissons les attributs de qualitébasés sur l’image qui sont les plus importants afin de définir un cadre commun.Afin de relier ces attributs de qualité aux vrais échantillons biométriques,nous développons une nouvelle base de données de qualité d’image biométriquemulti-modalité qui contient des images échantillons de haute qualité et des imagesdégradées pour l’empreinte digitale acquise sans contact, l’iris (dont l’acquisitionest réalisée dans le spectre visible) et le visage. Les types de dégradation appliquéssont liés aux attributs de qualité qui sont communs aux diverses modalitéset qui sont basés sur l’image. Un autre aspect important du cadre commun proposéest la qualité de l’image et ses applications en biométrie. Nous avons d’abordintroduit et classifié les métriques de qualité d’image existantes, puis effectué unbref aperçu des métriques de qualité d’image sans référence, qui peuvent être appliquéespour l’évaluation de la qualité des échantillons biométriques. De plus, nousétudions comment les mesures de qualité d’image sans référence ont été utiliséespour l’évaluation de la qualité des empreintes digitales, de l’iris et des modalitésbiométriques du visage.Des expériences pour l’évaluation de la performance des métriques de qualitéd’image sans référence sur les images de visage et de l’iris sont effectuées. Lesrésultats expérimentaux indiquent qu’il existe plusieurs métriques qui peuventévaluer la qualité des échantillons biométriques de l’iris et du visage avec un fortcoefficient de correlation. La méthode obtenant les meilleurs résultats en termede performance est ré-entrainée sur des images d’empreintes digitales, ce qui permetd’augmenter significativement les performances du système de reconnaissancebiométrique.À travers le travail réalisé dans cette thèse, nous avons démontré l’applicabilitédes métriques de qualité d’image sans référence pour l’évaluation d’échantillonsbiométriques multi-modalité non contraints<br>The aim of this research is to investigate multi-modality biometric image qualityassessment methods for unconstrained samples. Studies of biometrics noted thesignificance of sample quality for a recognition system or a comparison algorithmbecause the performance of the biometric system depends mainly on the qualityof the sample images. The need to assess the quality of multi-modality biometricsamples is increased with the requirement of a high accuracy multi-modalitybiometric systems.Following an introduction and background in biometrics and biometric samplequality, we introduce the concept of biometric sample quality assessment for multiplemodalities. Recently established ISO/IEC quality standards for fingerprint,iris, and face are presented. In addition, sample quality assessment approacheswhich are designed specific for contact-based and contactless fingerprint, nearinfrared-based iris and visible wavelength iris, as well as face are surveyed. Followingthe survey, approaches for the performance evaluation of biometric samplequality assessment methods are also investigated.Based on the knowledge gathered from the biometric sample quality assessmentchallenges, we propose a common framework for the assessment of multi-modalitybiometric image quality. We review the previous classification of image-basedquality attributes for a single biometric modality and investigate what are the commonimage-based attributes for multi-modality. Then we select and re-define themost important image-based quality attributes for the common framework. In order to link these quality attributes to the real biometric samples, we develop anew multi-modality biometric image quality database which has both high qualitysample images and degraded images for contactless fingerprint, visible wavelengthiris, and face modalities. The degradation types are based on the selected commonimage-based quality attributes. Another important aspect in the proposed commonframework is the image quality metrics and their applications in biometrics. Wefirst introduce and classify the existing image quality metrics and then conducteda brief survey of no-reference image quality metrics, which can be applied to biometricsample quality assessment. Plus, we investigate how no-reference imagequality metrics have been used for the quality assessment for fingerprint, iris, andface biometric modalities.The experiments for the performance evaluation of no-reference image qualitymetrics for visible wavelength face and iris modalities are conducted. The experimentalresults indicate that there are several no-reference image quality metricsthat can assess the quality of both iris and face biometric samples. Lastly, we optimizethe best metric by re-training it. The re-trained image quality metric canprovide better recognition performance than the original. Through the work carriedout in this thesis we have shown the applicability of no-reference image qualitymetrics for the assessment of unconstrained multi-modality biometric samples

With increased use of mobile phones that support mobile commerce, there is a need to examine the authentication of users. The password-based authentication techniques are not reliable with many passwords being too simple. A biometric authentication system is becoming more commonplace and is widely used in security fields because of its special stability and uniqueness. Within this context, the researcher has developed a fuzzy logic based multi-modal biometric authentication system to verify the identity of a mobile phone user. The research presented in this thesis involves three parts of work. Firstly, a model to support the authentication of mobile commerce has been proposed. Within this model, a number of different authentication levels have been defined in the system which sought to achieve the balance between usability and security. Secondly, the researcher has developed a multi-modal biometric authentication system which involves typing behaviour recognition, face recognition and speaker recognition techniques to establish the identity of the user on the mobile phone. However, there are some issues with deterministic biometric authentication systems. Because of this, a fuzzy logic model which can determine the transaction risk in m-commerce and the recognition result from biometric authentication engine has been built. In the experimental stage, the researcher simulates a mobile commerce environment. At one extreme, users will just want to obtain the item and not enter any identity. They are prepared to accept the low level of risk when the transaction is of low value. On the other extreme for a high value transaction users will accept multiple levels of security and would not want the transaction to go through without any checking. The experimental results showed that the fuzzy logic based multi-modal authentication system can achieve a low equal error rate (EER) of 0.63%, and by using the fuzzy logic model, it can effectively reduce the false rejection rate (FRR). There is also a reduction in the environmental influence in the fuzzy logic based biometric authentication. There are three contributions of the thesis: firstly, this research has proposed a model to support the authentication in mobile commerce. Secondly, a multi-modal biometric authentication system was developed. Another major contribution is the development of a fuzzy logic based multi-modal biometric authentication system which is able to overcome the issues of deterministic biometric systems. Overall, the results gained in this thesis prove that using the multi-modal biometric authentication system, itis possible to establish the identity of the user on a mobile phone. The fuzzy logic based authentication model can make the multi-modal biometric system more accurate, and also reduce the influence of external environmental factors. A holistic interpretation of the research indicated that the mobile security and smart system can help mobile commerce become more secure and more flexible in future.

Security breaches due to misidentification of an individual pose one of the greatest threats and challenges for today’s world. The use of biometrics can be quite promising in minimising this threat. Biometrics refers to the automatic authentication of individuals based on their physiological and behavioural characteristics. To date, various biometric systems have been proposed in the literature, among them biometric traits such as the face, iris, fingerprints, retina, gait, and vocal patterns are found to be distinctive to each and every person and are considered to be most reliable biometric identifiers. Regardless of the available biometrics traits, to date, no biometric system has been found to be a perfect, and which can be applied universally in a way that is robust/adaptive to change in different environmental conditions. Multimodal biometric systems were proposed in the late 1990’s to extend the range of biometric applicability. In a multimodal biometric system, two or more biometric identifiers are fused by an information fusion technique, thereby providing robustness for changing in a greater range of environmental conditions and enhancing other properties that an ideal biometric system should possess. Another important property that a biometric system should possess is a capability to distinguish between real and fake data. Although both the robustness of the system and capability to distinguish between a real and fake data should be incorporated into a single system, there is a trade-off. Therefore, due to the aforementioned research problems, this thesis addresses advancements in multimodal ocular biometrics using iris and sclera and also investigates the trade-off between robustness/adaptability and anti-spoofing/liveness detection (which is one method to distinguish between real and fake data). Biometrics traits that allow personal identification, eye traits offer a good choice of biometrics, as the eye offers a wide range of unique characteristics. The two common eye biometric identifiers that can be found in the literature are the iris and retina. Two more biometrics that are becoming popular nowadays are the sclera and the peri-ocular. The iris biometric is believed to be the most reliable eye biometric and that is why various commercial products based on this biometric are available; but the iris biometric used in an unconstrained scenario is still an open research area. The performance of iris biometrics with changes in the gaze angle of the eye can be affected highly. Therefore, due to this restriction, high user cooperation is required by persons with squinty eyes to get successfully identified in an iris biometric system. Identifying individuals with darker irises is another big challenge in iris recognition in the visible spectrum. To mitigate this problem, multi-modal eye biometrics was proposed by combining iris and sclera traits in the visible spectrum. However, in order to establish the concept of multimodal eye biometrics using the iris and sclera, it is first necessary to assess if sufficient discriminatory information can be gained from the sclera, further assessment in regards to its combination with the iris pattern and adaptiveness of the traits with respect to changes in environmental conditions, population, the data acquisition technique and time span. Multimodal biometrics using sclera and iris have not been extensively studied and little is known regarding their usefulness. So, the state-of-the-art related to it is not sufficiently mature and still in its infancy. This thesis concentrates on designing an image processing and pattern recognition module for evaluating the potential of the scleral biometric with regards to biometric accuracy. Thus, research is also carried out investigate usefulness of the sclera trait in combination with the iris pattern. Various, pre-processing techniques, segmentation, feature extraction, information fusion and classification techniques are employed to push the border of this multimodal biometrics. The latter half of the thesis concentrates on bridging the anti-spoofing technique liveliness with adaptiveness of biometrics. Traditional biometric systems are not equipped to distinguish between fake and real data that has been scanned in front of the sensors. As a result, they adhere to forgery attacks by intruders who can take the privilege of a genuine user. With the rising demand of involuntary or unmanned biometric systems in border security, flight checking, and other restricted zones, the incorporation of the automatic detection of forgery attacks is becoming very obvious. Adaptability of the system with respect to the change in the trait is another important aspect that this biometric system should be enriched with. As mentioned previously both the forgery detection method (termed as liveness detection in the literature of biometrics) and adaptability of the trait is necessary for a trusted involuntary biometric system, but initial studies in the literature exhibit it as a trade-off. Therefore to fulfil the gap, this thesis aimed to propose a new framework for software-based liveness detection, which is also associated to the adaptability of the trait. To fulfil the above-highlighted aim in the proposed framework, intra-class level (i.e. user level) liveness detection is introduced employing image quality-based features. Furthermore, to incorporate the adaptability of the trait, online learning-based classifiers are used. Initial investigation and experimental results solicit the use of the proposed framework for trusted involuntary biometric systems. Two new multi-angle eye datasets were developed and published as a part of the current research. The thesis also consists of contributions to other fields of pattern recognition such as wrist vein biometrics, multiscript signature verification and script identification.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Info & Comm Tech<br>Science, Environment, Engineering and Technology<br>Full Text

The issue of traditional user authentication methods, such as username/passwords, when accessing information systems, the Internet, and Web-based applications still pose significant vulnerabilities. The problem of user authentication including physical and logical access appears to have limited, if any, coverage in research from the perspective of biometric as ‘something the user knows.’ Previous methods of establishing ones’ identity by using a password, or presenting a token or identification (ID) card are vulnerable to circumvention by misplacement or unauthorized sharing. The need for reliable user authentication techniques has increased in the wake of heightened concerns about information security and rapid advancements in networking, communication, and mobility. The main goal of this research study was to examine the role of the authentication method (BIO-PIN™ or username/password) and time, on the effectiveness of authentication, as well as the users’ ability to remember the BIO-PIN™ versus username/password (UN/PW). Moreover, this study compared the BIO-PIN™ with a traditional multi-factor biometric authentication using multiple fingerprints (without sequence) and a numerical PIN sequence (noted as "BIO+PIN"). Additionally, this research study examined the authentication methods when controlled for age, gender, user’s computer experience, and number of accounts. This study used a quasi-experimental multiple baseline design method to evaluate the effectiveness of the BIO-PIN™ authentication method. The independent, dependent, and control variables were addressed using descriptive statistics and Multivariate Analysis of Variance (MANOVA) statistical analysis to compare the BIO-PIN™, the BIO+PIN, and UN/PW authentication methods for research questions (RQs) 1 and 2. Additionally, the Multivariate Analysis of Covariance (MANCOVA) was used to address RQ 3 and RQ4, which seeks to test any differences when controlled by age, gender, user experience, and number of accounts. This research study was conducted over a 10-week period with participant engagement occurring over time including a registration week and in intervals of 2 weeks, 3 weeks, and 5 weeks. This study advances the current research in multi-factor biometric authentication and increases the body of knowledge regarding users’ ability to remember industry standard UN/PWs, the BIO-PIN™ sequence, and traditional BIO+PIN.

Security and safety is one the main concerns both for governments and for private companies in the last years so raising growing interests and investments in the area of biometric recognition and video surveillance, especially after the sad happenings of September 2001. Outlays assessments of the U.S. government for the years 2001-2005 estimate that the homeland security spending climbed from $56.0 billions of dollars in 2001 to almost $100 billion of 2005. In this lapse of time, new pattern recognition techniques have been developed and, even more important, new biometric traits have been investigated and refined; besides the well-known physical and behavioral characteristics, also physiological measures have been studied, so providing more features to enhance discrimination capabilities of individuals. This dissertation proposes the design of a multimodal biometric platform, FAIRY, based on the following biometric traits: ear, face, iris EEG and ECG signals. In the thesis the modular architecture of the platform has been presented, together with the results obtained for the solution to the recognition problems related to the different biometrics and their possible fusion. Finally, an analysis of the pattern recognition issues concerning the area of videosurveillance has been discussed.

Identity verification systems are an important part of our every day life. A typical example is the Automatic Teller Machine (ATM) which employs a simple identity verification scheme: the user is asked to enter their secret password after inserting their ATM card; if the password matches the one prescribed to the card, the user is allowed access to their bank account. This scheme suffers from a major drawback: only the validity of the combination of a certain possession (the ATM card) and certain knowledge (the password) is verified. The ATM card can be lost or stolen, and the password can be compromised. Thus new verification methods have emerged, where the password has either been replaced by, or used in addition to, biometrics such as the person’s speech, face image or fingerprints. Apart from the ATM example described above, biometrics can be applied to other areas, such as telephone & internet based banking, airline reservations & check-in, as well as forensic work and law enforcement applications. Biometric systems based on face images and/or speech signals have been shown to be quite effective. However, their performance easily degrades in the presence of a mismatch between training and testing conditions. For speech based systems this is usually in the form of channel distortion and/or ambient noise; for face based systems it can be in the form of a change in the illumination direction. A system which uses more than one biometric at the same time is known as a multi-modal verification system; it is often comprised of several modality experts and a decision stage. Since a multi-modal system uses complimentary discriminative information, lower error rates can be achieved; moreover, such a system can also be more robust, since the contribution of the modality affected by environmental conditions can be decreased. This thesis makes several contributions aimed at increasing the robustness of single- and multi-modal verification systems. Some of the major contributions are listed below. The robustness of a speech based system to ambient noise is increased by using Maximum Auto-Correlation Value (MACV) features, which utilize information from the source part of the speech signal. A new facial feature extraction technique is proposed (termed DCT-mod2), which utilizes polynomial coefficients derived from 2D Discrete Cosine Transform (DCT) coefficients of spatially neighbouring blocks. The DCT-mod2 features are shown to be robust to an illumination direction change as well as being over 80 times quicker to compute than 2D Gabor wavelet derived features. The fragility of Principal Component Analysis (PCA) derived features to an illumination direction change is solved by introducing a pre-processing step utilizing the DCT-mod2 feature extraction. We show that the enhanced PCA technique retains all the positive aspects of traditional PCA (that is, robustness to compression artefacts and white Gaussian noise) while also being robust to the illumination direction change. Several new methods, for use in fusion of speech and face information under noisy conditions, are proposed; these include a weight adjustment procedure, which explicitly measures the quality of the speech signal, and a decision stage comprised of a structurally noise resistant piece-wise linear classifier, which attempts to minimize the effects of noisy conditions via structural constraints on the decision boundary.