Spectral imaging of human portraits and image quality

This dissertation addresses the problem of capturing spectral images for human portraits and evaluating image quality of spectral images. A new spectral imaging approach is proposed in this dissertation for spectral images of human portraits. Thorough statistical analysis is performed for spectral reflectances from various races and different face parts. A spectral imaging system has been designed and calibrated for human portraits. The calibrated imaging system has the ability to represent not only the facial skin but also the spectra of lips, eyes and hair from various races as well. The generated spectral images can be applied to color-imaging system design and analysis. To evaluate the image quality of spectral imaging systems, a visual psychophysical image quality experiment has been performed in this dissertation. The spectral images were simulated based on real spectral imaging system. Meaningful image quality results have been obtained for spectral images generated from different spectral imaging systems. To bridge the gap between the physical measures and subjective visual perceptions of image quality, four image distortion factors were defined. Image quality metrics were obtained and evaluated based statistical analysis and multiple analysis. The image quality metrics have high correlation with subjective assessment for image quality. The image quality contribution of the distortion factors were evaluated. As an extension of the work other researchers in MCSL have initiated, this dissertation research will, working with other researchers in MCSL, put effort to build a publicly accessible database of spectral images, Lippmann2000

Robust iris recognition under unconstrained settings

Tese de mestrado integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

Biometric iris image segmentation and feature extraction for iris recognition

PhD ThesisThe continued threat to security in our interconnected world today begs for urgent solution. Iris biometric like many other biometric systems provides an alternative solution to this lingering problem. Although, iris recognition have been extensively studied, it is nevertheless, not a fully solved problem which is the factor inhibiting its implementation in real world situations today. There exists three main problems facing the existing iris recognition systems: 1) lack of robustness of the algorithm to handle non-ideal iris images, 2) slow speed of the algorithm and 3) the applicability to the existing systems in real world situation. In this thesis, six novel approaches were derived and implemented to address these current limitation of existing iris recognition systems. A novel fast and accurate segmentation approach based on the combination of graph-cut optimization and active contour model is proposed to define the irregular boundaries of the iris in a hierarchical 2-level approach. In the first hierarchy, the approximate boundary of the pupil/iris is estimated using a method based on Hough’s transform for the pupil and adapted starburst algorithm for the iris. Subsequently, in the second hierarchy, the final irregular boundary of the pupil/iris is refined and segmented using graph-cut based active contour (GCBAC) model proposed in this work. The segmentation is performed in two levels, whereby the pupil is segmented first before the iris. In order to detect and eliminate noise and reflection artefacts which might introduce errors to the algorithm, a preprocessing technique based on adaptive weighted edge detection and high-pass filtering is used to detect reflections on the high intensity areas of the image while exemplar based image inpainting is used to eliminate the reflections. After the segmentation of the iris boundaries, a post-processing operation based on combination of block classification method and statistical prediction approach is used to detect any super-imposed occluding eyelashes/eyeshadows. The normalization of the iris image is achieved though the rubber sheet model. In the second stage, an approach based on construction of complex wavelet filters and rotation of the filters to the direction of the principal texture direction is used for the extraction of important iris information while a modified particle swam optimization (PSO) is used to select the most prominent iris features for iris encoding. Classification of the iriscode is performed using adaptive support vector machines (ASVM). Experimental results demonstrate that the proposed approach achieves accuracy of 98.99% and is computationally about 2 times faster than the best existing approach.Ebonyi State University and Education Task Fund, Nigeri

A Multimodal Biometric Authentication for Smartphones

Title from PDF of title page, viewed on October 18, 2016Dissertation advisor: Reza DerakhshaniVitaIncludes bibliographical references (pages 119-127)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2015Biometrics is seen as a viable solution to ageing password based authentication on smartphones. Fingerprint biometric is leading the biometric technology for smartphones, however, owing to its high cost, major players in mobile industry are introducing fingerprint sensors only on their flagship devices, leaving most of their other devices without a fingerprint sensor. Cameras on the other hand have been seeing a constant upgrade in sensor and supporting hardware, courtesy of ‘selfies’ on all smartphones. Face, iris and visible vasculature are three biometric traits that can be captured in visible spectrum using existing cameras on smartphone. Current biometric recognition systems on smartphones rely on a single biometric trait for faster authentication thereby increasing the probability of failure to enroll, affecting the usability of the biometric system for practical purposes. While multibiometric system mitigates this problem, computational models for multimodal biometrics recognition on smartphones have scarcely been studied. This dissertation provides a practical multimodal biometric solution for existing smartphones using iris, periocular and eye vasculature biometrics. In this work, computational methods for quality analysis and feature detection of biometric data that are suitable for deployment on smartphones have been introduced. A fast, efficient feature detection algorithm (Vascular Point Detector) for identifying interest points on images garnered from both rear and front facing camera has been developed. It was observed that the retention ratio of VPD for final similarity score calculation was at least 10% higher than state of art interest point detectors such as FAST, over various datasets. An interest point suppression algorithm based on local histograms was introduced, reducing the computational footprint of matching algorithm by at least 30%. Further, experiments are presented which successfully combine multiple samples of eye vasculature, iris and periocular biometrics obtained from a single smartphone camera sensor. Several methods are explored to test the effectiveness of multi-modal and multi algorithm fusion at various levels of biometric recognition process, with the best algorithms performing under 2 second on an IPhone 5s. It is noted that the multimodal biometric system outperforms the unimodal biometric systems in terms of both performance and failure to enroll rates.Introduction -- Biometric systems -- Database -- Eye vaculature recognition -- Iris recognition in visible wavelength on smartphones -- Periocular recognition on smartphones -- Conclusions and future wor

Unconstrained Iris Recognition

This research focuses on iris recognition, the most accurate form of biometric identification. The robustness of iris recognition comes from the unique characteristics of the human, and the permanency of the iris texture as it is stable over human life, and the environmental effects cannot easily alter its shape. In most iris recognition systems, ideal image acquisition conditions are assumed. These conditions include a near infrared (NIR) light source to reveal the clear iris texture as well as look and stare constraints and close distance from the capturing device. However, the recognition accuracy of the-state-of-the-art systems decreases significantly when these constraints are relaxed. Recent advances have proposed different methods to process iris images captured in unconstrained environments. While these methods improve the accuracy of the original iris recognition system, they still have segmentation and feature selection problems, which results in high FRR (False Rejection Rate) and FAR (False Acceptance Rate) or in recognition failure. In the first part of this thesis, a novel segmentation algorithm for detecting the limbus and pupillary boundaries of human iris images with a quality assessment process is proposed. The algorithm first searches over the HSV colour space to detect the local maxima sclera region as it is the most easily distinguishable part of the human eye. The parameters from this stage are then used for eye area detection, upper/lower eyelid isolation and for rotation angle correction. The second step is the iris image quality assessment process, as the iris images captured under unconstrained conditions have heterogeneous characteristics. In addition, the probability of getting a mis-segmented sclera portion around the outer ring of the iris is very high, especially in the presence of reflection caused by a visible wavelength light source. Therefore, quality assessment procedures are applied for the classification of images from the first step into seven different categories based on the average of their RGB colour intensity. An appropriate filter is applied based on the detected quality. In the third step, a binarization process is applied to the detected eye portion from the first step for detecting the iris outer ring based on a threshold value defined on the basis of image quality from the second step. Finally, for the pupil area segmentation, the method searches over the HSV colour space for local minima pixels, as the pupil contains the darkest pixels in the human eye. In the second part, a novel discriminating feature extraction and selection based on the Curvelet transform are introduced. Most of the state-of-the-art iris recognition systems use the textural features extracted from the iris images. While these fine tiny features are very robust when extracted from high resolution clear images captured at very close distances, they show major weaknesses when extracted from degraded images captured over long distances. The use of the Curvelet transform to extract 2D geometrical features (curves and edges) from the degraded iris images addresses the weakness of 1D texture features extracted by the classical methods based on textural analysis wavelet transform. Our experiments show significant improvements in the segmentation and recognition accuracy when compared to the-state-of-the-art results

Multibiometric System Combining Iris and Retina

Tato diplomová práce se zabývá multibiometrickými systémy, specificky potom biometrickou fúzí. Práce popisuje biometrii oka, tedy rozpoznávání na základě sítnice a duhovky. Stěžejní část tvoří návrh a implementace biometrického systému, který je založený na rozpoznání sítnice a duhovky.This diploma thesis focuses on multibiometric systems, specifically on biometric fusion. The thesis describes eye biometrics, i.e. recognition based on retina and iris. The key part consists of design and implementation specification of a biometric system based on retina and iris recognition.

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Evaluation of tumour-associated antigens to optically label cutaneous basal cell carcinoma for surgical excision

Basal cell carcinoma (BCC) is the most common skin cancer worldwide, with South Africa having the highest incidence rate only after Australia. The most effective treatment modality for BCC is tumor excision via Mohs surgery (pioneered by Dr. Frederic Mohs of the University of Wisconsin in 1930), a microscopically controlled surgery that removes a tumor piecemeal in layers until each layer is free of any neoplastic tissue. The major drawback of Mohs excision is that the surgeon might miss any neoplastic tissue as the tumor margin is not always well defined, and the tumor often could extend beyond the superficial layers of skin. Moreover, it's a time-consuming, expensive procedure that takes generally 3-4 h, at times even more, if several rounds of excisions are warranted. In South Africa, at the time of writing, therapy using the surgery cost around R45,000. The status quo thus necessitates identifying BCC cells both in the superficial layers and beyond the layers of the skin in individual patients. Our aim was to identify BCC-specific cell surface proteins and design, engineer, and test a range of SNAPtag–based antibody fusion proteins that would specifically bind to and detect such BCC cell surface receptors. The SNAP-tag antibody technology is based on the genetic fusion of a disease-specific ligand to a protein tag derived from O6-alkylguanine-DNA alkyltransferase, which would allow for covalent auto-labeling of the corresponding antibody based fusion proteins with benzylguanine-modified (BG) substrates (e.g., fluorophores) under physiological conditions with high efficiency at 1:1 stoichiometry. This would allow to develop a unique immunological screening modality which should allow to visually label BCC lesions for a more precise surgical excision. The best-performing SNAP-tag–based diagnostic antibodies resulting from these studies would be further evaluated in the future in suitable mouse models, thus aiming to reduce the time needed for surgical removal of BCC lesions and complete removal of the tumor from both superficial and deep layers of the skin by a single-excision procedure. We used an integrated computational tool to re-analyze publicly available cDNA microarray data in combination with theoretical search to identify BCC-associated antigens. Accordingly, six different antigens were selected and single-chain variable fragments (scFv) targeting these antigens were cloned in fusion with SNAP-tag encoding gene into a custom expression vector for production in a secretory mammalian system. scFv-SNAP-tag protein was isolated from the cell culture supernatant by immobilized metal affinity chromatography and eluted protein samples were analyzed by gel electrophoresis and immunoblotting. The absolute amount of the full-length protein was quantified by densitometry. Purified scFv-SNAP-tag proteins were validated for specific binding to corresponding antigen-positive cells by flow cytometry and confocal microscopy. Of the six different scFv-SNAP-tag fusion proteins cloned, four were successfully expressed in HEK293T cells. The specific binding to EpCAM, EMA, CSPG4, and CD138 antigenexpressing cell lines was observed on incubation with scFvUBS54-SNAP-tag, scFvID405- SNAP-tag, 9.2.27scFv-SNAP-tag, and scFvh-STL002-SNAP-tag, respectively. In addition, we showed the selective cell killing effect of scFvUBS54-SNAP after conjugating it with the cytotoxic drug BG-modified auristatin-F (BG-AF). In conclusion, we identified various cell surface antigens along with one possibly novel antigen for BCC detection and therapy. Further, we successfully designed and synthesized SNAP tag based antibody fusion proteins and showed their functional activity by selective binding to the corresponding antigens on the surface of tumor cells. Based on these findings, we presume that these antibodies can effectively bind to BCC and can confirm EpCAM as one of the target antigens, which has already been reported to be a standard immunophenotypic marker for differential BCC diagnosis

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Combination of optical coherence tomography and Raman spectroscopy

The two techniques, optical coherence tomography (OCT) and Raman spectroscopy allow visualization of structural changes of biological samples via cross-sectional images and corresponding spectral responses that represent chemical constituents, respectively. By combining the two modalities, the medical practitioner would be able to obtain a fast OCT scan followed by Raman spectroscopy point measurements as an identification step. This method not only promotes early diagnosis but also is a prelude to a point-of-care medical system. The combined microscopic OCT-Raman device was systematically characterized to assess performance and suitability for investigating non-melanoma skin cancer samples and atherosclerotic plaques. A pilot non-melanoma skin cancer study was conducted within the boundaries of the standard clinical workflow. This limited the total measurement time of ex vivo biopsies to 12- 15 minutes per sample which was sufficient for both modalities. By correlating the results from all sources i.e. microscopic, OCT, Raman spectroscopy, and histopathologic images, it was concluded that the combined device was able to differentiate between diseased and healthy tissue. OCT results correlated well with the gold standard and a general Raman spectral shape for basal cell carcinoma type of skin cancers was concluded. In terms of atherosclerotic plaques early as well as advanced plaques were investigated from ex vivo rabbit and human aorta samples, respectively. Early plaques were represented as bright spots by OCT and Raman spectroscopy revealed their constituents to be triglyceride rich regions. Plaques at a later stage were visualized as arterial blockages with bright inclusions of crystalline calcium deposits which were confirmed by Raman spectroscopy. An attempt to test the miniaturized application of the system, using separate fiber based systems under in vivo conditions on a rabbit model, early plaques were investigated