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

Wrist vascular biometric recognition using a portable contactless system

Human wrist vein biometric recognition is one of the least used vascular biometric modalities. Nevertheless, it has similar usability and is as safe as the two most common vascular variants in the commercial and research worlds: hand palm vein and finger vein modalities. Besides, the wrist vein variant, with wider veins, provides a clearer and better visualization and definition of the unique vein patterns. In this paper, a novel vein wrist non-contact system has been designed, implemented, and tested. For this purpose, a new contactless database has been collected with the software algorithm TGS-CVBR®. The database, called UC3M-CV1, consists of 1200 near-infrared contactless images of 100 different users, collected in two separate sessions, from the wrists of 50 subjects (25 females and 25 males). Environmental light conditions for the different subjects and sessions have been not controlled: different daytimes and different places (outdoor/indoor). The software algorithm created for the recognition task is PIS-CVBR®. The results obtained by combining these three elements, TGS-CVBR®, PIS-CVBR®, and UC3M-CV1 dataset, are compared using two other different wrist contact databases, PUT and UC3M (best value of Equal Error Rate (EER) = 0.08%), taken into account and measured the computing time, demonstrating the viability of obtaining a contactless real-time-processing wrist system.Publicad

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers

Improvements in Cardiac Spect/CT for the Purpose of Tracking Transplanted Cells

Regenerative therapy via stem cell transplantation has received increased attention to help treat the myocardial injury associated with heart disease. Currently, the hybridisation of SPECT with X-ray CT is expanding the utility of SPECT. This thesis compared two SPECT/CT systems for attenuation correction using slow or fast-CT attenuation maps (mu-maps). We then developed a method to localize transplanted cells in relation to compromised blood flow in the myocardium following a myocardial infarction using SPECT/CT. Finally, a method to correct for image truncation was studied for a new SPECT/CT design that incorporated small field-of-view (FOV) detectors. Computer simulations compared gated-SPECT reconstructions using slow-CT and fast-CT mu-maps with gated-CT mu-maps. Using fast-CT mu-maps improved the Root Mean Squared (RMS) error from 4.2% to 4.0%. Three canine experiments were performed comparing SPECT/CT reconstruction using the Infinia/Hawkeye-4 (slow-CT) and Symbia T6 (fast-CT). Canines were euthanized prior to imaging, and then ventilated. The results showed improvements in both RMS errors and correlation coefficients for all canines. A first-pass contrast CT imaging technique can identify regions of myocardial infarction and can be fused with SPECT. Ten canines underwent surgical ligation of the left-anterior-descending artery. Cells were labeled with 111In-tropolone and transplanted into the myocardium. SPECT/CT was performed on day of transplantation, 4, and 10 days post-transplantation. For each imaging session first-pass perfusion CT was performed and successfully delineated the infarct zone. Delayed-enhanced MRI was performed and correlated well with first-pass CT. Contrast-to-noise ratios were calculated for 111In-SPECT and suggested that cells can be followed for 11 effective half-lives. We evaluated a modified SPECT/CT acquisition and reconstruction method for truncated SPECT. Cardiac SPECT/CT scans were acquired in 14 patients. The original projections were truncated to simulate a small FOV acquisition. Data was reconstructed in three ways: non-truncated and standard reconstruction (NTOSEM), which was our gold-standard; truncated and standard reconstruction (TOSEM); and truncated and a modified reconstruction (TMOSEM). Compared with NTOSEM, small FOV imaging incurred an average cardiac count ratio error greater than 100% using TOSEM and 8.9% using TMOSEM. When we plotted NTOSEM against TOSEM and TMOSEM the correlation coefficient was 0.734 and 0.996 respectively

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 127, April 1974

This special bibliography lists 279 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1974

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Pose Invariant 3D Face Authentication based on Gaussian Fields Approach

This thesis presents a novel illuminant invariant approach to recognize the identity of an individual from his 3D facial scan in any pose, by matching it with a set of frontal models stored in the gallery. In view of today’s security concerns, 3D face reconstruction and recognition has gained a significant position in computer vision research. The non intrusive nature of facial data acquisition makes face recognition one of the most popular approaches for biometrics-based identity recognition. Depth information of a 3D face can be used to solve the problems of illumination and pose variation associated with face recognition. The proposed method makes use of 3D geometric (point sets) face representations for recognizing faces. The use of 3D point sets to represent human faces in lieu of 2D texture makes this method robust to changes in illumination and pose. The method first automatically registers facial point-sets of the probe with the gallery models through a criterion based on Gaussian force fields. The registration method defines a simple energy function, which is always differentiable and convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. The new method overcomes the necessity of close initialization and converges in much less iterations as compared to the Iterative Closest Point algorithm. The use of an optimization method, the Fast Gauss Transform, allows a considerable reduction in the computational complexity of the registration algorithm. Recognition is then performed by using the robust similarity score generated by registering 3D point sets of faces. Our approach has been tested on a large database of 85 individuals with 521 scans at different poses, where the gallery and the probe images have been acquired at significantly different times. The results show the potential of our approach toward a fully pose and illumination invariant system. Our method can be successfully used as a potential biometric system in various applications such as mug shot matching, user verification and access control, and enhanced human computer interaction