Background We analytically characterized the past, present, and future levels and trends of the national herpes simplex virus type 2 (HSV-2) epidemic in the United States. Methods A population-level mathematical model was constructed to describe HSV-2 transmission dynamics and was fitted to the data series of the National Health and Nutrition Examination Surveys. Results Over 1950-2050, antibody prevalence (seroprevalence) increased rapidly from 1960, peaking at 19.9% in 1983 in those aged 15-49, before reversing course to decline to 13.2% by 2020 and 8.5% by 2050. Incidence rate peaked in 1971 at 11.9 per 1,000 person-years, before declining by 59% by 2020 and 70% by 2050. Annual number of new infections peaked at 1,033,000 in 1978, before declining to 667,000 by 2020 and 600,000 by 2050. Women were disproportionately affected, averaging 75% higher seroprevalence, 95% higher incidence rate, and 71% higher annual number of infections. In 2020, 78% of infections were acquired by those 15-34 year-olds. Conclusions The epidemic has undergone a major transition over a century, with the greatest impact in those 15-34 year-olds. In addition to 47 million prevalent infections in 2020, high incidence will persist over the next three decades, adding >600,000 new infections every year.

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Abstract. Still-to-video face recognition (FR) is an important function in several video surveillance applications like watchlist screening, where faces captured over a network of video cameras are matched against reference stills belonging to target individuals. Screening of faces against a watchlist is a challenging problem due to variations in capturing conditions (e.g., pose and illumination), to camera inter-operability, and to the limited number of reference stills. In holistic approaches to FR, Local Binary Pattern (LBP) descriptors are often considered to represent facial captures and reference stills. Despite their efficiency, LBP descriptors are known as being sensitive to illumination changes. In this paper, the performance of still-to-video FR is compared when different passive illumination normalization techniques are applied prior to LBP feature extraction. This study focuses on representative retinex, self-quotient, diffusion, filtering, means de-noising, retina, wavelet and frequency-based techniques that are suitable for fast and accurate face screening. Experimental results obtained with videos from the Chokepoint dataset indicate that, although Multi-Scale Weberfaces and Tan and Triggs techniques tend to outperform others, the benefits of these techniques varies considerably according to the individual and illumination conditions. Results suggest that a combination of these techniques should be selected dynamically based on changing capture conditions.

Still-to-video face recognition (FR) systems for watchlist screening seek to recognize individuals of interest given faces captured over a network of video surveillance cameras. Screening faces against a watchlist is a challenging application because only a limited number of reference stills is available per individual during enrollment, and the appearance of face captures in videos changes from camera to camera, due to variations in illumination, pose, blur, scale, expression and occlusion. In order to improve the robustness of FR systems, several local matching techniques have been proposed that rely on static or dynamic weighting of patches. However, these approaches are not suitable for watchlist screening applications where the capturing conditions vary significantly over different camera fields of view (FoV). In this paper, a new dynamic weighting technique is proposed for weighting facial patches based on video data collected a priori from the specific operational domain (camera FoV) and on image quality assessment. Results obtained on videos from the Chokepoint dataset indicate that the proposed approach can significantly outperform the reference local matching methods because patch weights tend to grow for discriminant facial regions.