Biometric Face Recognition System using SURF Based Approach

Face recognition can be viewed as the problem of robustly identifying an image of a human face, given some database of known faces [6]. We propose a novel, SURF based approach to the problem of face recognition. Although the results are not gratifying our proposed approach loosens the burden of creating the sub spaces as is done in PCA, LDA and the most recent Bayesian approach. Also, during the experiments even though we used an unturned program for the proposed approach, it outperforms the basic PCA and LDA based approaches in terms of consistency. This article presents a scale-invariant and novel rotation detector and descriptor known as SURF (Speeded-Up Robust Features). SURF outperforms previously defined schemes with respect to repeatability as well as distinctiveness and robustness. It’s computing and comparing can be much faster. This is done by relying on integral images for image convolutions; by making the strengths of the leading existing detectors and descriptors (specifically, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. Its result is a combination of novel detection, description, and finding match steps. The paper contains an overview of the detector and descriptor and then finds out the effects of the most important parameters. The article is concluded with SURF’s application to two challenging. Yet it converse goals i.e. camera calibration which is a special case of image registration and recognition of objects. Our experiments show that SURF is very useful in vast areas of computer vision

Securing Cloud Storage by Transparent Biometric Cryptography

With the capability of storing huge volumes of data over the Internet, cloud storage has become a popular and desirable service for individuals and enterprises. The security issues, nevertheless, have been the intense debate within the cloud community. Significant attacks can be taken place, the most common being guessing the (poor) passwords. Given weaknesses with verification credentials, malicious attacks have happened across a variety of well-known storage services (i.e. Dropbox and Google Drive) – resulting in loss the privacy and confidentiality of files. Whilst today's use of third-party cryptographic applications can independently encrypt data, it arguably places a significant burden upon the user in terms of manually ciphering/deciphering each file and administering numerous keys in addition to the login password. The field of biometric cryptography applies biometric modalities within cryptography to produce robust bio-crypto keys without having to remember them. There are, nonetheless, still specific flaws associated with the security of the established bio-crypto key and its usability. Users currently should present their biometric modalities intrusively each time a file needs to be encrypted/decrypted – thus leading to cumbersomeness and inconvenience while throughout usage. Transparent biometrics seeks to eliminate the explicit interaction for verification and thereby remove the user inconvenience. However, the application of transparent biometric within bio-cryptography can increase the variability of the biometric sample leading to further challenges on reproducing the bio-crypto key. An innovative bio-cryptographic approach is developed to non-intrusively encrypt/decrypt data by a bio-crypto key established from transparent biometrics on the fly without storing it somewhere using a backpropagation neural network. This approach seeks to handle the shortcomings of the password login, and concurrently removes the usability issues of the third-party cryptographic applications – thus enabling a more secure and usable user-oriented level of encryption to reinforce the security controls within cloud-based storage. The challenge represents the ability of the innovative bio-cryptographic approach to generate a reproducible bio-crypto key by selective transparent biometric modalities including fingerprint, face and keystrokes which are inherently noisier than their traditional counterparts. Accordingly, sets of experiments using functional and practical datasets reflecting a transparent and unconstrained sample collection are conducted to determine the reliability of creating a non-intrusive and repeatable bio-crypto key of a 256-bit length. With numerous samples being acquired in a non-intrusive fashion, the system would be spontaneously able to capture 6 samples within minute window of time. There is a possibility then to trade-off the false rejection against the false acceptance to tackle the high error, as long as the correct key can be generated via at least one successful sample. As such, the experiments demonstrate that a correct key can be generated to the genuine user once a minute and the average FAR was 0.9%, 0.06%, and 0.06% for fingerprint, face, and keystrokes respectively. For further reinforcing the effectiveness of the key generation approach, other sets of experiments are also implemented to determine what impact the multibiometric approach would have upon the performance at the feature phase versus the matching phase. Holistically, the multibiometric key generation approach demonstrates the superiority in generating the bio-crypto key of a 256-bit in comparison with the single biometric approach. In particular, the feature-level fusion outperforms the matching-level fusion at producing the valid correct key with limited illegitimacy attempts in compromising it – 0.02% FAR rate overall. Accordingly, the thesis proposes an innovative bio-cryptosystem architecture by which cloud-independent encryption is provided to protect the users' personal data in a more reliable and usable fashion using non-intrusive multimodal biometrics.Higher Committee of Education Development in Iraq (HCED

Evaluation methodologies for security testing biometric systems beyond technological evaluation

The main objective of this PhD Thesis is the specification of formal evaluation methodologies for testing the security level achieved by biometric systems when these are working under specific contour conditions. This analysis is conducted through the calculation of the basic technical biometric system performance and its possible variations. To that end, the next two relevant contributions have been developed. The first contribution is the definition of two independent biometric performance evaluation methodologies for analysing and quantifying the influence of environmental conditions and human factors respectively. From the very beginning it has been claimed and demonstrated that these two contour conditions are the most significant parameters that may affect negatively the biometric performance. Nevertheless, in spite of ISO/IEC 19795 standard [ISO'06b], which addresses biometric performance testing and reporting, being published in 2006, no evaluation methodology for assessing such adverse effects has been implemented yet. Therefore, this dissertation proposes both methodologies which have been defined in accordance to the following requirements: - should be general and modality independent for covering the analysis of all kind of biometric systems; - should conform to the principles and requirements already defined in ISO/IEC 19795 multipart standard; and - should provide requirements and procedures to accurately define the evaluation conditions to be tested, conduct reproducible test methods and obtain objective and intercomparable results. The second relevant contribution is the development of detailed guidelines for addressing how to conduct biometric performance evaluations in compliance with Common Criteria [CC]. Common Criteria is currently the only international recognised evaluation framework with which developers have to analyse and demonstrate the level of security achieved by their products. However, the applicability of this methodology to biometrics needs the specification of supplementary guidelines. As a consequence, this dissertation proposes such guidelines which have been specified according to the following requirements: - should be independent of any biometric modality; - should be based on previous works published in this topic BTSE [BTSE'01], BEM [BEM'02] and the ISO/IEC 19792 international standard which addresses security evaluation of biometric system; - should conform to the last version of both Common Criteria and the ISO/IEC 19795 multipart standards; and - should cover those kinds of biometric performance evaluations that can be repeatable, i.e. technology and scenario evaluations as well as the Common Criteria evaluation activities involved in the execution of such test procedures. As for the evaluation of the security of biometric systems there is the need of determine their performance, and as such performance also depends on contour conditions, both evaluation methodologies (i.e. environmental and human factors) and Common Criteria guidelines, are merged in order to provide improved evaluation methodology for the security of biometric systems. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------El objetivo principal de esta Tesis Doctoral es la especificación de metodologías de evaluación formales para analizar el nivel de seguridad alcanzado por los sistemas biométricos cuando estos se encuentran trabajando bajo condiciones de contorno específicas. Este análisis se realiza a través del cálculo del rendimiento técnico básico del sistema biométrico y sus posibles variaciones. A tal efecto, se han elaborado las siguientes contribuciones. En primer lugar, se han especificado dos metodologías de evaluación de rendimiento biométrico de manera independiente para analizar y cuantificar la influencia de las condiciones ambientales y los factores humanos, respectivamente. Desde los primeros estudios sobre rendimiento biométrico, se ha afirmado y demostrado que éstos son los parámetros más significativos que pueden afectar negativamente al rendimiento biométrico. No obstante, a pesar de que la norma ISO/IEC 19795 que regula la evaluación y documentación del rendimiento de los sistemas biométricos fue publicada en 2006, ninguna metodología que evalúe dichos efectos adversos ha sido implementada hasta el momento. Por lo tanto la presente Tesis Doctoral propone ambas metodologías, las cuáles han sido definidas conforme a las siguientes condiciones: - son de carácter general e independientes de cualquier modalidad biométrica para cubrir el análisis de todo tipo de sistemas biométricos, - cumplen con los principios y requisitos previamente definidos en la norma internacional ISO/IEC 19795 [ISO'06b], y - proporcionan requisitos y procedimientos detallados para: definir las condiciones de los ensayos, efectuar métodos de ensayo reproducibles y obtener resultados objetivos e intercomparables. En segundo lugar, se han desarrollado directrices específicas que abordan la forma de realizar evaluaciones de rendimiento biométrico conforme a "Common Criteria for IT security evaluation" (conocido habitualmente como "Common Criteria" [CC]). Common Criteria es actualmente el único marco de evaluación internacionalmente reconocido del que disponen los desarrolladores de sistemas biométricos para analizar y demostrar el nivel de seguridad que alcanzan sus productos. Sin embargo, la aplicación de esta metodología a la tecnología biométrica requiere la especificación de pautas complementarias. Por consiguiente, esta Tesis Doctoral propone tales pautas o directrices, las cuáles se han especificado de acuerdo con los siguientes requisitos: - son independientes de cualquier modalidad biométrica, - se basan en los trabajos previos que ya han sido publicados en esta área tales como BTSE [BTSE'01], BEM [BEM'02] y el estándar internacional ISO/IEC 19792 [ISO'09a] que regula la evaluación de seguridad de los sistemas biométricos, - son conformes a las últimas versiones tanto de Common Criteria como de la norma internacional ISO/IEC 19795, y - cubren tanto el tipo de evaluaciones de rendimiento biométrico que pueden ser repetibles, es decir las evaluaciones tecnológicas y de escenario, como las actividades de evaluación establecidas por la norma Common Criteria que conllevan la realización de dichos procedimientos de test. Debido a que es necesario determinar el rendimiento de los sistemas biométricos para evaluar su seguridad, y ya que dicho rendimiento depende de distintas condiciones de contorno, las dos metodologías de evaluación previamente definidas (condiciones ambientales y factores humanos) se han unido con las directrices de Common Criteria, para así conseguir una mejora sustancial en la metodología de evaluación de la seguridad de los sistemas biométricos

Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. Robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy and disease-aware imaging. In addition to enhancing diagnostic outcomes, RUSS also holds the potential to provide medical interventions for populations suffering from the shortage of experienced sonographers. In this paper, we categorize RUSS as teleoperated or autonomous. Regarding teleoperated RUSS, we summarize their technical developments, and clinical evaluations, respectively. This survey then focuses on the review of recent work on autonomous robotic US imaging. We demonstrate that machine learning and artificial intelligence present the key techniques, which enable intelligent patient and process-specific, motion and deformation-aware robotic image acquisition. We also show that the research on artificial intelligence for autonomous RUSS has directed the research community toward understanding and modeling expert sonographers' semantic reasoning and action. Here, we call this process, the recovery of the "language of sonography". This side result of research on autonomous robotic US acquisitions could be considered as valuable and essential as the progress made in the robotic US examination itself. This article will provide both engineers and clinicians with a comprehensive understanding of RUSS by surveying underlying techniques.Comment: Accepted by Medical Image Analysi

Sok: Security and privacy in implantable medical devices and body area networks.

Abstract-Balancing security, privacy, safety, and utility is a necessity in the health care domain, in which implantable medical devices (IMDs) and body area networks (BANs) have made it possible to continuously and automatically manage and treat a number of health conditions. In this work, we survey publications aimed at improving security and privacy in IMDs and health-related BANs, providing clear definitions and a comprehensive overview of the problem space. We analyze common themes, categorize relevant results, and identify trends and directions for future research. We present a visual illustration of this analysis that shows the progression of IMD/BAN research and highlights emerging threats. We identify three broad research categories aimed at ensuring the security and privacy of the telemetry interface, software, and sensor interface layers and discuss challenges researchers face with respect to ensuring reproducibility of results. We find that while the security of the telemetry interface has received much attention in academia, the threat of software exploitation and the sensor interface layer deserve further attention. In addition, we observe that while the use of physiological values as a source of entropy for cryptographic keys holds some promise, a more rigorous assessment of the security and practicality of these schemes is required

Bayesian hierarchical modeling for the forensic evaluation of handwritten documents

The analysis of handwritten evidence has been used widely in courts in the United States since the 1930s (Osborn, 1946). Traditional evaluations are conducted by trained forensic examiners. More recently, there has been a movement toward objective and probability-based evaluation of evidence, and a variety of governing bodies have made explicit calls for research to support the scientific underpinnings of the field (National Research Council, 2009; President\u27s Council of Advisors on Science and Technology (US), 2016; National Institutes of Standards and Technology). This body of work makes contributions to help satisfy those needs for the evaluation of handwritten documents. We develop a framework to evaluate a questioned writing sample against a finite set of genuine writing samples from known sources. Our approach is fully automated, reducing the opportunity for cognitive biases to enter the analysis pipeline through regular examiner intervention. Our methods are able to handle all writing styles together, and result in estimated probabilities of writership based on parametric modeling. We contribute open-source datasets, code, and algorithms. A document is prepared for the evaluation processed by first being scanned and stored as an image file. The image is processed and the text within is decomposed into a sequence of disjoint graphical structures. The graphs serve as the smallest unit of writing we will consider, and features extracted from them are used as data for modeling. Chapter 2 describes the image processing steps and introduces a distance measure for the graphs. The distance measure is used in a K-means clustering algorithm (Forgy, 1965; Lloyd, 1982; Gan and Ng, 2017), which results in a clustering template with 40 exemplar structures. The primary feature we extract from each graph is a cluster assignment. We do so by comparing each graph to the template and making assignments based on the exemplar to which each graph is most similar in structure. The cluster assignment feature is used for a writer identification exercise using a Bayesian hierarchical model on a small set of 27 writers. In Chapter 3 we incorporate new data sources and a larger number of writers in the clustering algorithm to produce an updated template. A mixture component is added to the hierarchical model and we explore the relationship between a writer\u27s estimated mixing parameter and their writing style. In Chapter 4 we expand the hierarchical model to include other graph-based features, in addition to cluster assignments. We incorporate an angular feature with support on the polar coordinate system into the hierarchical modeling framework using a circular probability density function. The new model is applied and tested in three applications