Incidence and significance of an elevated red blood cell distribution width among hospitalised HIV-infected adult patients

We audited the records of unselected hospitalised HIV-positive adults admitted to a University-affiliated inner London hospital to identify the frequency of elevated red blood cell distribution width (RDW), and potential associations with specific diagnoses, and with outcome. Of 259 patients audited, 188 (73%) were men. Patients' median age was 47 years (interquartile range = 41-54). An elevated RDW was seen in 50 patients (19%); 200 (77%) had an elevated C-reactive protein (CRP), and 77 (30%) had a low haemoglobin. Only five patients had an elevated RDW without an elevated CRP and/or low haemoglobin. An elevated RDW was associated with a wide range of infectious, inflammatory, and malignant conditions similar to observed associations reported in the general non-HIV infected adult population. Additionally an elevated RDW occurred both in patients with well-controlled HIV infection and in receipt of antiretroviral therapy, as well as in those with newly diagnosed and poorly-controlled infection. Five (10%) of 50 patients with an elevated RDW needed intensive care unit (ICU) admission and two (4%) died. Two (0.95%) of 209 patients with a normal RDW needed ICU admission and four (1.9%) died. The findings of this audit are limited by the relatively small number of patients and the single site nature of the audit

Using age information as a soft biometric trait for face image analysis

Soft biometrics refers to a group of traits that can provide some information about an individual but are inadequate for identification or recognition purposes. Age, as an important soft biometric trait, can be inferred based on the appearance of human faces. However, compared to other facial attributes like race and gender, age is rather subtle due to the underlying conditions of individuals (i.e., their upbringing environment and genes). These uncertainties make age-related face image analysis (including age estimation, age synthesis and age-invariant face recognition) still unsolved. Specifically, age estimation is concerned with inferring the specific age from human face images. Age synthesis is concerned with the rendering of face images with natural ageing or rejuvenating effects. Age-invariant face recognition involves the recognition of the identity of subjects correctly regardless of their age. Recently, thanks to the rapid development of machine learning, especially deep learning, age-related face image analysis has gained much more attention from the research community than ever before. Deep learning based models that deal with age-related face image analysis have also significantly boosted performance compared to models that only use traditional machine learning methods, such as decision trees or boost algorithms. In this chapter, we first introduce the concepts and theory behind the three main areas of age-related face image analysis and how they can be used in practical biometric applications. Then, we analyse the difficulties involved in these applications and summarise the recent progress by reviewing the state-of-the-art methods involving deep learning. Finally, we discuss the future research trends and the issues that are not addressed by existing works. We also discuss the relationship among these three areas and show how solutions within one area can help to tackle issues in the others

Automatic age and gaze estimation under uncontrolled conditions

Can the computer learn a person’s age just from analyzing facial images obtained by a simple webcam? If so, you can automatically determine if a person has the right age to buy beer (shops), to use a credit card (retail), and can enter bars (entertainment). Can the computer automatically recognize where people are looking at? This is very useful if you want to automatically determine the viewing of a person when looking at websites, products in shopping malls, and the road when driving a car. This thesis addresses these two questions by providing algorithms for age prediction and eye gaze estimation. Different from previous approaches, the focus is to provide (practical) solutions for real-world applications. Automatic age estimation may be hindered by many different factors. One of the main problems is facial expressions as they create expression-dependent face wrinkles confusing the real age wrinkles. Moreover, poor imaging conditions may introduce noise that may affect the accuracy of the estimated age. We go beyond the standard scenarios to address these cases. Typical gaze estimation systems require calibration which involves following explicit instructions by the user such as tracing a moving dot on the screen. Such prerequisite is not feasible for some practical applications such as shopping malls to determine where the people are looking at. In this thesis, algorithms are developed to estimate the gaze points without the need for a calibration by the user

Age estimation under changes in image quality: An experimental study

In this paper, we investigate the influence of image quality on the performance of aging features. Age estimation systems used or designed a number of aging features to capture the aging cues from the face such as skin texture and wrinkles. These aging cues are sensitive to small changes in the imaging conditions which suggests considering the imaging quality when extracting such information. Although interesting performances are reported on various datasets, the effect of image quality has not been addressed. We introduce a scheme to explore the influence of image quality on the performance of appearance aging features. A number of datasets are experimented on where artifacts resulted from different types of noise are considered. Finally, we propose a method to automatically apply the most suitable features based on the quality of the image. The results show that better or comparable performance is obtained when automatically applying different features, based on image quality, in comparison to a single (best) feature type