A Survey of the methods on fingerprint orientation field estimation

Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): Accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods

Fingerabdruckswachstumvorhersage, Bildvorverarbeitung und Multi-level Judgment Aggregation

Im ersten Teil dieser Arbeit wird Fingerwachstum untersucht und eine Methode zur Vorhersage von Wachstum wird vorgestellt. Die Effektivität dieser Methode wird mittels mehrerer Tests validiert. Vorverarbeitung von Fingerabdrucksbildern wird im zweiten Teil behandelt und neue Methoden zur Schätzung des Orientierungsfelds und der Ridge-Frequenz sowie zur Bildverbesserung werden vorgestellt: Die Line Sensor Methode zur Orientierungsfeldschätzung, gebogene Regionen zur Ridge-Frequenz-Schätzung und gebogene Gabor Filter zur Bildverbesserung. Multi-level Jugdment Aggregation wird eingeführt als Design Prinzip zur Kombination mehrerer Methoden auf mehreren Verarbeitungsstufen. Schließlich wird Score Neubewertung vorgestellt, um Informationen aus der Vorverarbeitung mit in die Score Bildung einzubeziehen. Anhand eines Anwendungsbeispiels wird die Wirksamkeit dieses Ansatzes auf den verfügbaren FVC-Datenbanken gezeigt.Finger growth is studied in the first part of the thesis and a method for growth prediction is presented. The effectiveness of the method is validated in several tests. Fingerprint image preprocessing is discussed in the second part and novel methods for orientation field estimation, ridge frequency estimation and image enhancement are proposed: the line sensor method for orientation estimation provides more robustness to noise than state of the art methods. Curved regions are proposed for improving the ridge frequency estimation and curved Gabor filters for image enhancement. The notion of multi-level judgment aggregation is introduced as a design principle for combining different methods at all levels of fingerprint image processing. Lastly, score revaluation is proposed for incorporating information obtained during preprocessing into the score, and thus amending the quality of the similarity measure at the final stage. A sample application combines all proposed methods of the second part and demonstrates the validity of the approach by achieving massive verification performance improvements in comparison to state of the art software on all available databases of the fingerprint verification competitions (FVC)

Signal processing and machine learning techniques for human verification based on finger textures

PhD ThesisIn recent years, Finger Textures (FTs) have attracted considerable attention as potential biometric characteristics. They can provide robust recognition performance as they have various human-speci c features, such as wrinkles and apparent lines distributed along the inner surface of all ngers. The main topic of this thesis is verifying people according to their unique FT patterns by exploiting signal processing and machine learning techniques. A Robust Finger Segmentation (RFS) method is rst proposed to isolate nger images from a hand area. It is able to detect the ngers as objects from a hand image. An e cient adaptive nger segmentation method is also suggested to address the problem of alignment variations in the hand image called the Adaptive and Robust Finger Segmentation (ARFS) method. A new Multi-scale Sobel Angles Local Binary Pattern (MSALBP) feature extraction method is proposed which combines the Sobel direction angles with the Multi-Scale Local Binary Pattern (MSLBP). Moreover, an enhanced method called the Enhanced Local Line Binary Pattern (ELLBP) is designed to e ciently analyse the FT patterns. As a result, a powerful human veri cation scheme based on nger Feature Level Fusion with a Probabilistic Neural Network (FLFPNN) is proposed. A multi-object fusion method, termed the Finger Contribution Fusion Neural Network (FCFNN), combines the contribution scores of the nger objects. The veri cation performances are examined in the case of missing FT areas. Consequently, to overcome nger regions which are poorly imaged a method is suggested to salvage missing FT elements by exploiting the information embedded within the trained Probabilistic Neural Network (PNN). Finally, a novel method to produce a Receiver Operating Characteristic (ROC) curve from a PNN is suggested. Furthermore, additional development to this method is applied to generate the ROC graph from the FCFNN. Three databases are employed for evaluation: The Hong Kong Polytechnic University Contact-free 3D/2D (PolyU3D2D), Indian Institute of Technology (IIT) Delhi and Spectral 460nm (S460) from the CASIA Multi-Spectral (CASIAMS) databases. Comparative simulation studies con rm the e ciency of the proposed methods for human veri cation. The main advantage of both segmentation approaches, the RFS and ARFS, is that they can collect all the FT features. The best results have been benchmarked for the ELLBP feature extraction with the FCFNN, where the best Equal Error Rate (EER) values for the three databases PolyU3D2D, IIT Delhi and CASIAMS (S460) have been achieved 0.11%, 1.35% and 0%, respectively. The proposed salvage approach for the missing feature elements has the capability to enhance the veri cation performance for the FLFPNN. Moreover, ROC graphs have been successively established from the PNN and FCFNN.the ministry of higher education and scientific research in Iraq (MOHESR); the Technical college of Mosul; the Iraqi Cultural Attach e; the active people in the MOHESR, who strongly supported Iraqi students

Development and application of Nuclear Magnetic Resonance spectroscopy and chemometric methods for the analysis of the metabolome of Saccharomyces cerevisiae under different growing conditions

[eng] Nuclear Magnetic Resonance (NMR) spectroscopy is able to produce by a single direct measurement a very high amount of chemical information. However, this information is not always easy to interpret. In fact, the complexity of the NMR spectral data analysis is proportional to the number of compounds present simultaneously in the analyzed sample, as resonances from different compounds overlap. One of the most extreme situations can be found for NMR spectra of samples from metabolomics studies, from which approximately fifty compounds can be detected in a single measurement. In the study of the chemical processes involving metabolites (metabolomics), the most commonly used NMR spectra are the one-dimensional proton (1D 1H) NMR spectra, since they are relatively fast to acquire and proton sensitivity is the highest. The 1H-13C Heteronuclear Single Quantum Coherence (HSQC) NMR spectra are also frequently used in metabolomics for an improved structural characterization of the detected metabolites. In this Thesis, we have developed different data analysis strategies of 1H NMR and 1H-13C HSQC NMR metabolomics datasets. The investigated NMR spectra were acquired from extracts of Saccharomyces cerevisiae cells previously exposed to different environmental perturbations. The aim of these studies was to better understand the different metabolic processes that regulate the yeast metabolism acclimation to different growing conditions. From the study of these NMR metabolomics experiments, we designed new strategies and protocols for the analysis of these datasets that include the steps of data import, data pre-treatment, resonance assignment and metabolite quantification. Moreover, different chemometric methods were applied for the identification of the possible biomarkers that define the metabolic states of yeast cells and to extract the main metabolic profiles that describe the observed changes in the metabolome. Furthermore, two chemometric strategies were proposed for the untargeted analysis of 1H NMR and 1H-13C HSQC NMR, respectively. For the study of 1H NMR spectra of metabolomics samples, the application of the Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) chemometric method allowed the satisfactory resolution of the individual 1H NMR spectra and concentrations of the different metabolites. On the other hand, the investigation of metabolomics datasets by 1H-13C HSQC NMR revealed that most of the data values in these NMR spectra are only descriptive of noise, hampering their chemometric data analysis. In this context, a new strategy to filter the variables relative to noise, named ‘Variables of Interest’ (or VOI) is proposed. After the application of this procedure, we observed that the analysis of the noise-filtered 1H-13C HSQC NMR spectra produced similar results to the corresponding analysis of 1H NMR spectra. Due to the existence of the second dimension in the 1H-13C HSQC NMR spectra, resonances are less overlapped and they could be integrated without using deconvolution approaches. For all these reasons, and linked to the fact that more chemical information is contained in the 1H-13C HSQC NMR spectra than in the 1H NMR spectra, the analysis of noise-filtered 1H-13C HSQC NMR spectra allow a more accurate characterization of the metabolomic system, in a reduced amount of time in comparison to the analysis of the corresponding 1H NMR spectra.[cat] L'espectroscòpia de ressonància magnètica nuclear (RMN) és capaç de generar mitjançant una mesura simple i directa una gran quantitat d'informació química. Tanmateix, aquesta informació no sempre és fàcil d'interpretar. De fet, la complexitat de l'anàlisi espectral és proporcional al nombre de compostos presents en la mostra analitzada, ja que les ressonàncies dels diferents compostos es troben superposades. Una de les situacions més extremes la podem trobar en el cas dels espectres de RMN de mostres obtingudes en estudis de metabolòmica, en les que es poden arribar a detectar al voltant d’una cinquantena de compostos en una sola mesura. En l'estudi dels processos químics relacionats amb els metabòlits (metabolòmica), els espectres de RMN més utilitzats són els espectres monodimensionals de protó (1D 1H), ja que són relativament ràpids d'adquirir i la sensibilitat del protó és la més alta. És també corrent utilitzar en estudis de metabolòmica els espectres de RMN bidimensionals 1H-13C heteronuclears de coherència quàntica única (2D 1H-13C HSQC), els quals permeten obtenir una millor caracterització estructural dels metabòlits detectats. En aquesta Tesi, s’han desenvolupat diferents estratègies d'anàlisi d’espectres de RMN de 1H i de 1H-13C HSQC de mostres de metabolòmica. Els espectres de RMN van ser adquirits d’extractes de llevat Saccharomyces cerevisiae que prèviament havia estat exposat a diferents pertorbacions mediambientals. L’objectiu d’aquests estudis ha estat millorar la comprensió dels diferents processos metabòlics que regulen l'aclimatació de les cèl·lules de llevat a diferents condicions de creixement. A partir d’aquests estudis de metabolòmica realitzats, es van dissenyar noves estratègies i protocols d'anàlisi de dades de RMN que inclouen la seva importació, el seu preprocessament, l'assignació de les ressonàncies i la seva integració. A més, es van aplicar diferents mètodes quimiomètrics que van permetre identificar els biomarcadors de l’estat metabòlic de les cèl·lules del llevat i extreure els principals perfils metabòlics que descriuen els canvis en el seu metabolisme. Es van proposar a més, dues estratègies quimiomètriques per a l’anàlisi no dirigida d’espectres de RMN de 1H i de 1H-13C HSQC, respectivament. En el cas dels estudis d’espectres de RMN de 1H, l'aplicació del mètode de resolució multivariant de corbes per mínims quadrats alternats (MCR-ALS) va permetre resoldre satisfactòriament les concentracions i els espectres individuals dels diferents metabòlits. D’altra banda, la investigació de l’estructura de les dades dels espectres de RMN de 1H-13C HSQC va revelar que la majoria dels valors espectrals són descriptius del soroll, cosa que dificulta la seva anàlisi. En aquest context, s’ha desenvolupat una nova estratègia per filtrar les variables descriptives del soroll, anomenada selecció de les variables d'interès (Variables of Interest, VOI). Després d’aplicar aquest procediment, es va observar que l'anàlisi dels espectres 1H-13C HSQC filtrats produeix resultats similars als obtinguts amb els espectres corresponents de 1H. Degut a l’existència de la segona dimensió en els espectres de 1H-13C HSQC, les ressonàncies estan menys solapades i es poden integrar sense fer servir estratègies basades en la seva deconvolució. Degut a tot això i al fet que els espectres de 1H-13C HSQC contenen més informació química que els de 1H, l’anàlisi dels espectres de 1H-13C HSQC filtrats amb aquest procediment permet una caracterització del sistema metabolòmic més acurada i amb temps d’anàlisis més curts, en comparació a l’anàlisi dels espectres de 1H corresponents

CONTACTLESS FINGERPRINT BIOMETRICS: ACQUISITION, PROCESSING, AND PRIVACY PROTECTION

Biometrics is defined by the International Organization for Standardization (ISO) as \u201cthe automated recognition of individuals based on their behavioral and biological characteristics\u201d Examples of distinctive features evaluated by biometrics, called biometric traits, are behavioral characteristics like the signature, gait, voice, and keystroke, and biological characteristics like the fingerprint, face, iris, retina, hand geometry, palmprint, ear, and DNA. The biometric recognition is the process that permits to establish the identity of a person, and can be performed in two modalities: verification, and identification. The verification modality evaluates if the identity declared by an individual corresponds to the acquired biometric data. Differently, in the identification modality, the recognition application has to determine a person's identity by comparing the acquired biometric data with the information related to a set of individuals. Compared with traditional techniques used to establish the identity of a person, biometrics offers a greater confidence level that the authenticated individual is not impersonated by someone else. Traditional techniques, in fact, are based on surrogate representations of the identity, like tokens, smart cards, and passwords, which can easily be stolen or copied with respect to biometric traits. This characteristic permitted a wide diffusion of biometrics in different scenarios, like physical access control, government applications, forensic applications, logical access control to data, networks, and services. Most of the biometric applications, also called biometric systems, require performing the acquisition process in a highly controlled and cooperative manner. In order to obtain good quality biometric samples, the acquisition procedures of these systems need that the users perform deliberate actions, assume determinate poses, and stay still for a time period. Limitations regarding the applicative scenarios can also be present, for example the necessity of specific light and environmental conditions. Examples of biometric technologies that traditionally require constrained acquisitions are based on the face, iris, fingerprint, and hand characteristics. Traditional face recognition systems need that the users take a neutral pose, and stay still for a time period. Moreover, the acquisitions are based on a frontal camera and performed in controlled light conditions. Iris acquisitions are usually performed at a distance of less than 30 cm from the camera, and require that the user assume a defined pose and stay still watching the camera. Moreover they use near infrared illumination techniques, which can be perceived as dangerous for the health. Fingerprint recognition systems and systems based on the hand characteristics require that the users touch the sensor surface applying a proper and uniform pressure. The contact with the sensor is often perceived as unhygienic and/or associated to a police procedure. This kind of constrained acquisition techniques can drastically reduce the usability and social acceptance of biometric technologies, therefore decreasing the number of possible applicative contexts in which biometric systems could be used. In traditional fingerprint recognition systems, the usability and user acceptance are not the only negative aspects of the used acquisition procedures since the contact of the finger with the sensor platen introduces a security lack due to the release of a latent fingerprint on the touched surface, the presence of dirt on the surface of the finger can reduce the accuracy of the recognition process, and different pressures applied to the sensor platen can introduce non-linear distortions and low-contrast regions in the captured samples. Other crucial aspects that influence the social acceptance of biometric systems are associated to the privacy and the risks related to misuses of biometric information acquired, stored and transmitted by the systems. One of the most important perceived risks is related to the fact that the persons consider the acquisition of biometric traits as an exact permanent filing of their activities and behaviors, and the idea that the biometric systems can guarantee recognition accuracy equal to 100\% is very common. Other perceived risks consist in the use of the collected biometric data for malicious purposes, for tracing all the activities of the individuals, or for operating proscription lists. In order to increase the usability and the social acceptance of biometric systems, researchers are studying less-constrained biometric recognition techniques based on different biometric traits, for example, face recognition systems in surveillance applications, iris recognition techniques based on images captured at a great distance and on the move, and contactless technologies based on the fingerprint and hand characteristics. Other recent studies aim to reduce the real and perceived privacy risks, and consequently increase the social acceptance of biometric technologies. In this context, many studies regard methods that perform the identity comparison in the encrypted domain in order to prevent possible thefts and misuses of biometric data. The objective of this thesis is to research approaches able to increase the usability and social acceptance of biometric systems by performing less-constrained and highly accurate biometric recognitions in a privacy compliant manner. In particular, approaches designed for high security contexts are studied in order improve the existing technologies adopted in border controls, investigative, and governmental applications. Approaches based on low cost hardware configurations are also researched with the aim of increasing the number of possible applicative scenarios of biometric systems. The privacy compliancy is considered as a crucial aspect in all the studied applications. Fingerprint is specifically considered in this thesis, since this biometric trait is characterized by high distinctivity and durability, is the most diffused trait in the literature, and is adopted in a wide range of applicative contexts. The studied contactless biometric systems are based on one or more CCD cameras, can use two-dimensional or three-dimensional samples, and include privacy protection methods. The main goal of these systems is to perform accurate and privacy compliant recognitions in less-constrained applicative contexts with respect to traditional fingerprint biometric systems. Other important goals are the use of a wider fingerprint area with respect to traditional techniques, compatibility with the existing databases, usability, social acceptance, and scalability. The main contribution of this thesis consists in the realization of novel biometric systems based on contactless fingerprint acquisitions. In particular, different techniques for every step of the recognition process based on two-dimensional and three-dimensional samples have been researched. Novel techniques for the privacy protection of fingerprint data have also been designed. The studied approaches are multidisciplinary since their design and realization involved optical acquisition systems, multiple view geometry, image processing, pattern recognition, computational intelligence, statistics, and cryptography. The implemented biometric systems and algorithms have been applied to different biometric datasets describing a heterogeneous set of applicative scenarios. Results proved the feasibility of the studied approaches. In particular, the realized contactless biometric systems have been compared with traditional fingerprint recognition systems, obtaining positive results in terms of accuracy, usability, user acceptability, scalability, and security. Moreover, the developed techniques for the privacy protection of fingerprint biometric systems showed satisfactory performances in terms of security, accuracy, speed, and memory usage

Development and application of Nuclear Magnetic Resonance spectroscopy and chemometric methods for the analysis of the metabolome of Saccharomyces cerevisiae under different growing conditions

[eng] Nuclear Magnetic Resonance (NMR) spectroscopy is able to produce by a single direct measurement a very high amount of chemical information. However, this information is not always easy to interpret. In fact, the complexity of the NMR spectral data analysis is proportional to the number of compounds present simultaneously in the analyzed sample, as resonances from different compounds overlap. One of the most extreme situations can be found for NMR spectra of samples from metabolomics studies, from which approximately fifty compounds can be detected in a single measurement. In the study of the chemical processes involving metabolites (metabolomics), the most commonly used NMR spectra are the one-dimensional proton (1D 1H) NMR spectra, since they are relatively fast to acquire and proton sensitivity is the highest. The 1H-13C Heteronuclear Single Quantum Coherence (HSQC) NMR spectra are also frequently used in metabolomics for an improved structural characterization of the detected metabolites. In this Thesis, we have developed different data analysis strategies of 1H NMR and 1H-13C HSQC NMR metabolomics datasets. The investigated NMR spectra were acquired from extracts of Saccharomyces cerevisiae cells previously exposed to different environmental perturbations. The aim of these studies was to better understand the different metabolic processes that regulate the yeast metabolism acclimation to different growing conditions. From the study of these NMR metabolomics experiments, we designed new strategies and protocols for the analysis of these datasets that include the steps of data import, data pre-treatment, resonance assignment and metabolite quantification. Moreover, different chemometric methods were applied for the identification of the possible biomarkers that define the metabolic states of yeast cells and to extract the main metabolic profiles that describe the observed changes in the metabolome. Furthermore, two chemometric strategies were proposed for the untargeted analysis of 1H NMR and 1H-13C HSQC NMR, respectively. For the study of 1H NMR spectra of metabolomics samples, the application of the Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) chemometric method allowed the satisfactory resolution of the individual 1H NMR spectra and concentrations of the different metabolites. On the other hand, the investigation of metabolomics datasets by 1H-13C HSQC NMR revealed that most of the data values in these NMR spectra are only descriptive of noise, hampering their chemometric data analysis. In this context, a new strategy to filter the variables relative to noise, named ‘Variables of Interest’ (or VOI) is proposed. After the application of this procedure, we observed that the analysis of the noise-filtered 1H-13C HSQC NMR spectra produced similar results to the corresponding analysis of 1H NMR spectra. Due to the existence of the second dimension in the 1H-13C HSQC NMR spectra, resonances are less overlapped and they could be integrated without using deconvolution approaches. For all these reasons, and linked to the fact that more chemical information is contained in the 1H-13C HSQC NMR spectra than in the 1H NMR spectra, the analysis of noise-filtered 1H-13C HSQC NMR spectra allow a more accurate characterization of the metabolomic system, in a reduced amount of time in comparison to the analysis of the corresponding 1H NMR spectra.[cat] L'espectroscòpia de ressonància magnètica nuclear (RMN) és capaç de generar mitjançant una mesura simple i directa una gran quantitat d'informació química. Tanmateix, aquesta informació no sempre és fàcil d'interpretar. De fet, la complexitat de l'anàlisi espectral és proporcional al nombre de compostos presents en la mostra analitzada, ja que les ressonàncies dels diferents compostos es troben superposades. Una de les situacions més extremes la podem trobar en el cas dels espectres de RMN de mostres obtingudes en estudis de metabolòmica, en les que es poden arribar a detectar al voltant d’una cinquantena de compostos en una sola mesura. En l'estudi dels processos químics relacionats amb els metabòlits (metabolòmica), els espectres de RMN més utilitzats són els espectres monodimensionals de protó (1D 1H), ja que són relativament ràpids d'adquirir i la sensibilitat del protó és la més alta. És també corrent utilitzar en estudis de metabolòmica els espectres de RMN bidimensionals 1H-13C heteronuclears de coherència quàntica única (2D 1H-13C HSQC), els quals permeten obtenir una millor caracterització estructural dels metabòlits detectats. En aquesta Tesi, s’han desenvolupat diferents estratègies d'anàlisi d’espectres de RMN de 1H i de 1H-13C HSQC de mostres de metabolòmica. Els espectres de RMN van ser adquirits d’extractes de llevat Saccharomyces cerevisiae que prèviament havia estat exposat a diferents pertorbacions mediambientals. L’objectiu d’aquests estudis ha estat millorar la comprensió dels diferents processos metabòlics que regulen l'aclimatació de les cèl·lules de llevat a diferents condicions de creixement. A partir d’aquests estudis de metabolòmica realitzats, es van dissenyar noves estratègies i protocols d'anàlisi de dades de RMN que inclouen la seva importació, el seu preprocessament, l'assignació de les ressonàncies i la seva integració. A més, es van aplicar diferents mètodes quimiomètrics que van permetre identificar els biomarcadors de l’estat metabòlic de les cèl·lules del llevat i extreure els principals perfils metabòlics que descriuen els canvis en el seu metabolisme. Es van proposar a més, dues estratègies quimiomètriques per a l’anàlisi no dirigida d’espectres de RMN de 1H i de 1H-13C HSQC, respectivament. En el cas dels estudis d’espectres de RMN de 1H, l'aplicació del mètode de resolució multivariant de corbes per mínims quadrats alternats (MCR-ALS) va permetre resoldre satisfactòriament les concentracions i els espectres individuals dels diferents metabòlits. D’altra banda, la investigació de l’estructura de les dades dels espectres de RMN de 1H-13C HSQC va revelar que la majoria dels valors espectrals són descriptius del soroll, cosa que dificulta la seva anàlisi. En aquest context, s’ha desenvolupat una nova estratègia per filtrar les variables descriptives del soroll, anomenada selecció de les variables d'interès (Variables of Interest, VOI). Després d’aplicar aquest procediment, es va observar que l'anàlisi dels espectres 1H-13C HSQC filtrats produeix resultats similars als obtinguts amb els espectres corresponents de 1H. Degut a l’existència de la segona dimensió en els espectres de 1H-13C HSQC, les ressonàncies estan menys solapades i es poden integrar sense fer servir estratègies basades en la seva deconvolució. Degut a tot això i al fet que els espectres de 1H-13C HSQC contenen més informació química que els de 1H, l’anàlisi dels espectres de 1H-13C HSQC filtrats amb aquest procediment permet una caracterització del sistema metabolòmic més acurada i amb temps d’anàlisis més curts, en comparació a l’anàlisi dels espectres de 1H corresponents

Securing health monitoring via body-centric time-frequency signature authorization

Identity-based attacks serve as the basis of an intruder’s attempt to launch security infringements in mobile health monitoring scenarios. Wireless channel perturbations due to the presence of human body are a relative phenomenon depending heavily on the subject’s dielectric properties. A new Body-Centric Signature Authorization (B-CSAI) approach based on time-frequency domain characteristics was proposed. This method utilizes multiple millimeter wave bands of 27-28 GHz, 29-30 GHz, and 31-32 GHz, thereby enhancing the security in body-centric communications exploiting benefits of subject specific channel signature. The proposed bornprint method is based on the intrinsic identity related time-frequency domain information, which generated by the user’s natural hand motion signature and resulting creeping waves and space waves. It can meet the unconditional keyless authorization requirements. A detailed measurement campaign considering radiation efficiency (η = -25.8, -24.7, -26.4), pathloss exponent, and shadowing factor in three millimeter wave bands, using six human subjects confirm the usability and efficiency of the proposed approach. This also shows that there is a wide space for realizing security from physical mechanisms

Machine Intelligence for Advanced Medical Data Analysis: Manifold Learning Approach

In the current work, linear and non-linear manifold learning techniques, specifically Principle Component Analysis (PCA) and Laplacian Eigenmaps, are studied in detail. Their applications in medical image and shape analysis are investigated. In the first contribution, a manifold learning-based multi-modal image registration technique is developed, which results in a unified intensity system through intensity transformation between the reference and sensed images. The transformation eliminates intensity variations in multi-modal medical scans and hence facilitates employing well-studied mono-modal registration techniques. The method can be used for registering multi-modal images with full and partial data. Next, a manifold learning-based scale invariant global shape descriptor is introduced. The proposed descriptor benefits from the capability of Laplacian Eigenmap in dealing with high dimensional data by introducing an exponential weighting scheme. It eliminates the limitations tied to the well-known cotangent weighting scheme, namely dependency on triangular mesh representation and high intra-class quality of 3D models. In the end, a novel descriptive model for diagnostic classification of pulmonary nodules is presented. The descriptive model benefits from structural differences between benign and malignant nodules for automatic and accurate prediction of a candidate nodule. It extracts concise and discriminative features automatically from the 3D surface structure of a nodule using spectral features studied in the previous work combined with a point cloud-based deep learning network. Extensive experiments have been conducted and have shown that the proposed algorithms based on manifold learning outperform several state-of-the-art methods. Advanced computational techniques with a combination of manifold learning and deep networks can play a vital role in effective healthcare delivery by providing a framework for several fundamental tasks in image and shape processing, namely, registration, classification, and detection of features of interest