Iris Recognition: The Consequences of Image Compression

Iris recognition for human identification is one of the most accurate biometrics, and its employment is expanding globally. The use of portable iris systems, particularly in law enforcement applications, is growing. In many of these applications, the portable device may be required to transmit an iris image or template over a narrow-bandwidth communication channel. Typically, a full resolution image (e.g., VGA) is desired to ensure sufficient pixels across the iris to be confident of accurate recognition results. To minimize the time to transmit a large amount of data over a narrow-bandwidth communication channel, image compression can be used to reduce the file size of the iris image. In other applications, such as the Registered Traveler program, an entire iris image is stored on a smart card, but only 4 kB is allowed for the iris image. For this type of application, image compression is also the solution. This paper investigates the effects of image compression on recognition system performance using a commercial version of the Daugman iris2pi algorithm along with JPEG-2000 compression, and links these to image quality. Using the ICE 2005 iris database, we find that even in the face of significant compression, recognition performance is minimally affected

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

Semi-supervised cross-entropy clustering with information bottleneck constraint

In this paper, we propose a semi-supervised clustering method, CEC-IB, that models data with a set of Gaussian distributions and that retrieves clusters based on a partial labeling provided by the user (partition-level side information). By combining the ideas from cross-entropy clustering (CEC) with those from the information bottleneck method (IB), our method trades between three conflicting goals: the accuracy with which the data set is modeled, the simplicity of the model, and the consistency of the clustering with side information. Experiments demonstrate that CEC-IB has a performance comparable to Gaussian mixture models (GMM) in a classical semi-supervised scenario, but is faster, more robust to noisy labels, automatically determines the optimal number of clusters, and performs well when not all classes are present in the side information. Moreover, in contrast to other semi-supervised models, it can be successfully applied in discovering natural subgroups if the partition-level side information is derived from the top levels of a hierarchical clustering

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans

Techniques for Ocular Biometric Recognition Under Non-ideal Conditions

The use of the ocular region as a biometric cue has gained considerable traction due to recent advances in automated iris recognition. However, a multitude of factors can negatively impact ocular recognition performance under unconstrained conditions (e.g., non-uniform illumination, occlusions, motion blur, image resolution, etc.). This dissertation develops techniques to perform iris and ocular recognition under challenging conditions. The first contribution is an image-level fusion scheme to improve iris recognition performance in low-resolution videos. Information fusion is facilitated by the use of Principal Components Transform (PCT), thereby requiring modest computational efforts. The proposed approach provides improved recognition accuracy when low-resolution iris images are compared against high-resolution iris images. The second contribution is a study demonstrating the effectiveness of the ocular region in improving face recognition under plastic surgery. A score-level fusion approach that combines information from the face and ocular regions is proposed. The proposed approach, unlike other previous methods in this application, is not learning-based, and has modest computational requirements while resulting in better recognition performance. The third contribution is a study on matching ocular regions extracted from RGB face images against that of near-infrared iris images. Face and iris images are typically acquired using sensors operating in visible and near-infrared wavelengths of light, respectively. To this end, a sparse representation approach which generates a joint dictionary from corresponding pairs of face and iris images is designed. The proposed joint dictionary approach is observed to outperform classical ocular recognition techniques. In summary, the techniques presented in this dissertation can be used to improve iris and ocular recognition in practical, unconstrained environments

Investigating neuroinflammatory disease through retinal imaging and biomarkers

Neuroinflammatory diseases, in particular multiple sclerosis (MS) and neuromyelitis optica spectrum disorder, often affect the anterior visual pathways. This can occur through direct inflammatory insult in the form of optic neuritis or through retrograde degeneration, but progressive neurodegenerative processes related to axonal loss and atrophy also play a role. Energy failure has been postulated as an important factor mediating factor in these neurodegenerative processes, but its exact role is poorly understood. The advent of optical coherence tomography (OCT) enables high resolution imaging of the retina with relative ease. In neurology research, OCT has mostly been used to quantify retinal layer thicknesses. This thesis focuses on the largely unexplored potential of OCT as a functional biomarker. The primary aim is to develop indirect non-invasive in-vivo biomarkers informing on metabolic function, taking into account the high energy demand of the retina, particularly during dark-adaptation. First, two novel functional OCT measures are presented; the dynamic dark-adaptation related thickening of the outer retinal layers and the relative reflectivity of the ellipsoid zone (EZ), which comprises the majority of retinal mitochondria. Both measures appeared to be reduced in acute optic neuritis, and also in chronic neuroinflammatory disease in the case of EZ reflectivity. Furthermore, pilot OCT-angiography (OCTA) data indicated that vascular density was reduced in acute optic neuritis. As reduced EZ reflectivity and lower vascular density were present to a similar degree in both eyes of acute optic neuritis patients suggest that a background level of mitochondrial dysfunction and hypoperfusion may occur in neuroinflammatory disease, independent from acute inflammatory activity. The work presented in this thesis illustrates that OCT has the potential to provide valuable information on retinal function in neuroinflammatory disease. In the future, artificial intelligence and big data analysis may enable the development of a holistic analysis method for raw OCT data, providing a summary report on both qualitative, such as presence of microcystic macular oedema (MMO), and quantitative scan features, such as layer thickness, vascular density and reflectivity. Comprehensive analysis of both functional and structural OCT data may facilitate diagnosis, inform on prognosis and provide important insight into the role of metabolic failure in the pathophysiology of neuroinflammatory disease