Analysis and synthesis of iris images

Of all the physiological traits of the human body that help in personal identification, the iris is probably the most robust and accurate. Although numerous iris recognition algorithms have been proposed, the underlying processes that define the texture of irises have not been extensively studied. In this thesis, multiple pair-wise pixel interactions have been used to describe the textural content of the iris image thereby resulting in a Markov Random Field (MRF) model for the iris image. This information is expected to be useful for the development of user-specific models for iris images, i.e. the matcher could be tuned to accommodate the characteristics of each user\u27s iris image in order to improve matching performance. We also use MRF modeling to construct synthetic irises based on iris primitive extracted from real iris images. The synthesis procedure is deterministic and avoids the sampling of a probability distribution making it computationally simple. We demonstrate that iris textures in general are significantly different from other irregular textural patterns. Clustering experiments indicate that the synthetic irises generated using the proposed technique are similar in textural content to real iris images

Utility of a Machine-Guided Tool for Assessing Risk Behavior Associated With Contracting HIV in Three Sites in South Africa: Protocol for an In-Field Evaluation

BackgroundMobile technology has helped to advance health programs, and studies have shown that an automated risk prediction model can successfully be used to identify patients who exhibit a high probable risk of contracting human immunodeficiency virus (HIV). A machine-guided tool is an algorithm that takes a set of subjective and objective answers from a simple questionnaire and computes an HIV risk assessment score. ObjectiveThe primary objective of this study is to establish that machine learning can be used to develop machine-guided tools and give us a deeper statistical understanding of the correlation between certain behavioral patterns and HIV. MethodsIn total, 200 HIV-negative adult individuals across three South African study sites each (two semirural and one urban) will be recruited. Study processes will include (1) completing a series of questions (demographic, sexual behavior and history, personal, lifestyle, and symptoms) on an application system, unaided (assistance will only be provided upon user request); (2) two HIV tests (one per study visit) being performed by a nurse/counselor according to South African national guidelines (to evaluate the prediction accuracy of the tool); and (3) communicating test results and completing a user experience survey questionnaire. The output metrics for this study will be computed by using the participants’ risk assessment scores as “predictions” and the test results as the “ground truth.” Analyses will be completed after visit 1 and then again after visit 2. All risk assessment scores will be used to calculate the reliability of the machine-guided tool. ResultsEthical approval was received from the University of Witwatersrand Human Research Ethics Committee (HREC; ethics reference no. 200312) on August 20, 2020. This study is ongoing. Data collection has commenced and is expected to be completed in the second half of 2021. We will report on the machine-guided tool’s performance and usability, together with user satisfaction and recommendations for improvement. ConclusionsMachine-guided risk assessment tools can provide a cost-effective alternative to large-scale HIV screening and help in providing targeted counseling and testing to prevent the spread of HIV. Trial RegistrationSouth African National Clinical Trial Registry DOH-27-042021-679; https://sanctr.samrc.ac.za/TrialDisplay.aspx?TrialID=5545 International Registered Report Identifier (IRRID)DERR1-10.2196/3030