A high efficient biometrics approach for unconstrained iris segmentation and recognition

This dissertation develops an innovative approach towards less-constrained iris biometrics. Two major contributions are made in this research endeavor: (1) Designed an award-winning segmentation algorithm in the less-constrained environment where image acquisition is made of subjects on the move and taken under visible lighting conditions, and (2) Developed a pioneering iris biometrics method coupling segmentation and recognition of the iris based on video of moving persons under different acquisitions scenarios. The first part of the dissertation introduces a robust and fast segmentation approach using still images contained in the UBIRIS (version 2) noisy iris database. The results show accuracy estimated at 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at 97% in a Noisy Iris Challenge Evaluation (NICE.I) in an international competition that involved 97 participants worldwide involving 35 countries, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time. The second part of this dissertation presents an innovative segmentation and recognition approach using video-based iris images. Following the segmentation stage which delineates the iris region through a novel segmentation strategy, some pioneering experiments on the recognition stage of the less-constrained video iris biometrics have been accomplished. In the video-based and less-constrained iris recognition, the test or subject iris videos/images and the enrolled iris images are acquired with different acquisition systems. In the matching step, the verification/identification result was accomplished by comparing the similarity distance of encoded signature from test images with each of the signature dataset from the enrolled iris images. With the improvements gained, the results proved to be highly accurate under the unconstrained environment which is more challenging. This has led to a false acceptance rate (FAR) of 0% and a false rejection rate (FRR) of 17.64% for 85 tested users with 305 test images from the video, which shows great promise and high practical implications for iris biometrics research and system design.

An incremental multilinear system for human face learning and recognition

This dissertation establishes a novel system for human face learning and recognition based on incremental multilinear Principal Component Analysis (PCA). Most of the existing face recognition systems need training data during the learning process. The system as proposed in this dissertation utilizes an unsupervised or weakly supervised learning approach, in which the learning phase requires a minimal amount of training data. It also overcomes the inability of traditional systems to adapt to the testing phase as the decision process for the newly acquired images continues to rely on that same old training data set. Consequently when a new training set is to be used, the traditional approach will require that the entire eigensystem will have to be generated again. However, as a means to speed up this computational process, the proposed method uses the eigensystem generated from the old training set together with the new images to generate more effectively the new eigensystem in a so-called incremental learning process. In the empirical evaluation phase, there are two key factors that are essential in evaluating the performance of the proposed method: (1) recognition accuracy and (2) computational complexity. In order to establish the most suitable algorithm for this research, a comparative analysis of the best performing methods has been carried out first. The results of the comparative analysis advocated for the initial utilization of the multilinear PCA in our research. As for the consideration of the issue of computational complexity for the subspace update procedure, a novel incremental algorithm, which combines the traditional sequential Karhunen-Loeve (SKL) algorithm with the newly developed incremental modified fast PCA algorithm, was established. In order to utilize the multilinear PCA in the incremental process, a new unfolding method was developed to affix the newly added data at the end of the previous data. The results of the incremental process based on these two methods were obtained to bear out these new theoretical improvements. Some object tracking results using video images are also provided as another challenging task to prove the soundness of this incremental multilinear learning method.

An Analysis of Stock Market Performance:The Dow Jones Industrial Average and the Three Top Performing Lodging Firma: 1982-1988

In their dialogue - An Analysis of Stock Market Performance: The Dow Jones Industrial Average and the Three Top Performing Lodging Firms 1982 – 1988 - by N. H. Ringstrom, Professor and Elisa S. Moncarz, Associate Professor, School of Hospitality Management at Florida International University, Professors Ringstrom and Moncarz state at the outset: “An interesting comparison can be made between the Dow Jones lndustrial Average and the three top performing, publicly held lodging firms which had $100 million or more in annual lodging revenues. The authors provide that analytical comparison with Prime Motor Inns Inc., the Marriott Corporation, and Hilton Hotels Corporation.” “Based on a criterion of size, only those with $100 million in annual lodging revenues or more resulted in the inclusion of the following six major hotel firms: Prime Motor Inns, Inc., Marriott Corporation, Hilton Hotels Corporation, Ramada Inc., Holiday Corporation and La Quinta Motor Inns, Inc.,” say Professors Ringstrom and Moncarz in framing this discussion with its underpinnings in the years 1982 to 1988. The article looks at each company’s fiscal and Dow Jones performance for the years in question, and presents a detailed analysis of said performance. Graphic analysis is included. It helps to have a fairly vigorous knowledge of stock market and fiscal examination criteria to digest this material. The Ringstrom and Moncarz analysis of Prime Motor Inns Incorporated occupies the first 7 pages of this article in and of itself. Marriot Corporation also occupies a prominent position in this discussion. “Marriott, a giant in the hospitality industry, is huge and continuing to grow. Its 1987 sales were more than $6.5 billion, and its employees numbered over 200,000 individuals, which place Marriott among the 10 largest private employers in the country,” Ringstrom and Moncarz parse Marriott’s influence as a significant financial player. “The firm has a fantastic history of growth over the past 60 years, starting in May 1927 with a nine-seat A & W Root Beer stand in Washington, D.C.,” offer the authors in initialing Marriot’s portion of the discussion with a brief history lesson. The Marriot firm was officially incorporated as Hot Shoppes Inc. in 1929. As the thesis statement for the discussion suggests the performance of these huge, hospitality giants is compared and contrasted directly to the Dow Jones Industrial Average performance. Reasons and empirical data are offered by the authors to explain the distinctions. It would be difficult to explain those distinctions without delving deeply into corporate financial history and the authors willingly do so in an effort to help you understand the growth, as well as some of the setbacks of these hospitality based juggernauts. Ringstrom and Moncarz conclude the article with an extensive overview and analysis of the Hilton Hotels Corporation performance for the period outlined. It may well be the most fiscally dynamic of the firms presented for your perusal. “It is interesting to note that Hilton Hotels Corporation maintained a very strong financial position with relatively little debt during the years 1982-1988…the highest among all companies in the study,” the authors paint.

The Military-Industrial Complex and US Military Spending After 9/11

This article examines the economic, political and institutional power of the military-industrial complex (MIC) by examining its influence on military spending before and after the events of 9/11. The reasons for the continuity of MIC influence in US foreign policy is explored. This includes the role of military contractors in financing policy planning organizations, the relationship between military contractors and the Defense Department, and the centralization of executive branch authority in foreign policy decision-making, especially during critical junctures or foreign policy crises.