Multimodal Biometric Person Authentication Using Face, Ear and Periocular Region Based on Convolution Neural Networks

Biometrics is an active area of research because of the increase in need for accurate person identification in numerous applications ranging from entertainment to security. Unimodal and multimodal are the well-known biometric methods. Unimodal biometrics uses one biometric modality of a person for person identification. The performance of an unimodal biometric system is degraded due to certain limitations such as: intra-class variations and nonuniversality. The person identification using more than one biometric modality of a person is multimodal biometrics. This method of identification has gained more interest due to resistance on spoof attacks and more recognition rate. Conventional methods of feature extraction have difficulty in engineering features that are liable to more variations such as illumination, pose and age variations. Feature extraction using convolution neural network (CNN) can overcome these difficulties because large dataset with robust variations can be used for training, where CNN can learn these variations. In this paper, we propose multimodal biometrics at feature level horizontal fusion using face, ear and periocular region biometric modalities and apply deep learning CNN for feature representation and also we propose face, ear and periocular region dataset that are robust to intra-class variations. The evaluation of the system is made by using proposed database. Accuracy, Precision, Recall and [Formula: see text] score are calculated to evaluate the performance of the system and had shown remarkable improvement over existing biometric system.

Etiology and Epidemiology of Maturity-onset Diabetes of the Young

Evidence indicates that Maturity-onset diabetes of the young (MODY) exhibits an autosomal dominant inheritance and is the most common type of monogenic diabetes. However, it should be noted that misdiagnosis of the condition is very common, as patients are usually mistaken for both types I and type II diabetes mellitus. In the present study, we have discussed the etiology, pathogenesis, and epidemiology of MODY based on an extensive literature review. Genetic mutations are mainly attributed to the development of the disease, which usually manifests throughout the second to fifth decades of life. Pancreatic islet cell destruction, impaired insulin secretion, defects regarding threshold to serum glucose levels, and other pathological events are usually observed in these patients. Data regarding the epidemiology of the condition is not adequately reported in the literature, especially among non-European populations, indicating the need to conduct future investigations. Ethnic and age variations are potentially epidemiological characteristics of the disease. However, not enough data are present in the literature to support such conclusions.

Kinship verification and recognition based on handcrafted and deep learning feature-based techniques

Background and Objectives Kinship verification and recognition (KVR) is the machine’s ability to identify the genetic and blood relationship and its degree between humans’ facial images. The face is used because it is one of the most significant ways to recognize each other. Automatic KVR is an interesting area for investigation. It greatly affects real-world applications, such as searching for lost family members, forensics, and historical and genealogical studies. This paper presents a comprehensive survey that describes KVR applications and kinship types. It presents a literature review of current studies starting from handcrafted passing through shallow metric learning and ending with deep learning feature-based techniques. Furthermore, kinship mostly used datasets are discussed that in turn open the way for future directions for the research in this field. Also, the KVR limitations are discussed, such as insufficient illumination, noise, occlusion, and age variations problems. Finally, future research directions are presented, such as age and gender variation problems. Methods We applied a literature survey methodology to retrieve data from academic databases. An inclusion and exclusion criteria were set. Three stages were followed to select articles. Finally, the main KVR stages, along with the main methods in each stage, were presented. We believe that surveys can help researchers easily to detect areas that require more development and investigation. Results It was found that handcrafted, metric learning, and deep learning were widely utilized in kinship verification and recognition problem using facial images. Conclusions Despite the scientific efforts that aim to address this hot research topic, many future research areas require investigation, such as age and gender variation. In the end, the presented survey makes it easier for researchers to identify the new areas that require more investigation and research.

Neighborhood Environments and Cognitive Decline in Middle and Old Age in China: Gender and Age Variations

Older adults are more vulnerable to neighborhood physical and social conditions due to longer exposure, increased vulnerability, changing spatial use, and a greater reliance on access to community sources of integration. Previous research has demonstrated an association between neighborhood environments and cognitive function in older adults. However, most studies were cross-sectional, focused on western countries, and did not examine potential moderating factors. This study examined gender and age variations in the relationship between neighborhood environments and cognitive decline in middle and old age in a developing country that is experiencing rapid population aging and rising prevalence of Alzheimer’s disease and related dementias. Using data from a nationally representative sample of adults aged 45 years and older from the three waves of China Health and Retirement Longitudinal Study (CHARLS 2011-2015), this study estimated multilevel growth curve models for the effects of neighborhood environments on cognitive decline separately for men and women and for those aged 45 to 64 and those aged 65 and above. It showed that the cross-sectional effect of outdoor facility and longitudinal effect of handicapped access were more significant for men, but the cross-sectional effect of community social participation and longitudinal effects of raining days, number of disasters, employment service, and community SES were more significant for women. The cross-sectional effect of infrastructure advantages and longitudinal effects of employment service and old age income support were more significant for adults aged 65 and over. These findings suggest that community-level interventions may be more beneficial for older women.

Metabolic risk factors for non-communicable diseases in Ethiopia: a systematic review and meta-analysis

ObjectivesNon-communicable diseases (NCDs) are causing a new and yetsignificant health challenge in low-income countries. In Ethiopia, although 39% of deaths are NCD related, the health system remains underprepared, highlighting the clear need for evidence on risk factor distributions to inform resource planning and the health response. Therefore, this review investigates prevalence distributions and sex and age variations of metabolic risk factors among Ethiopian adults.Research design and methodsThis systematic review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies published until 6 January 2021 were searched from PubMed, Scopus, ProQuest and Web of Science databases, reference lists of selected studies and grey literature. Studies reporting prevalence of metabolic risk factors: overweight/obesity, hypertension, impaired glucose homoeostasis and metabolic syndrome among Ethiopian adults were eligible for this systematic review and meta-analysis. Two authors independently extracted data and used the Joanna Briggs Institute tool for quality appraisal. The random effects model was used to conduct meta-analysis using Stata V.16. Subgroup analyses examined prevalence differences by region, study year, sample size and settings.ResultsFrom 6087 records, 74 studies including 104 382 participants were included. Most showed high prevalence of metabolic risk factors. Meta-analysis revealed pooled prevalence of metabolic risk factors from 12% to 24% with the highest prevalence observed for overweight/obesity (23.9%, 95% CI 19.9% to 28.0%) and hypertension (21.1%, 95% CI 18.7% to 23.5%), followed by metabolic syndrome (14.7%, 95% CI 9.8% to 19.6%) and impaired glucose tolerance (12.4%, 95% CI 8.7% to 16.1%). The prevalence of overweight/obesity was higher in women. All metabolic risk factors were higher among people aged above 45 years.ConclusionsA signficant proportion of Ethiopian adults have at least one metabolic risk factor for NCDs. Despite heterogeneity of studies limiting the certainty of evidence, the result suggests the need for coordinated effort among policymakers, healthcare providers, non-governmental stakeholders and the community to implement appropriate preventive measures to reduce these factors.

High-order functional interactions in ageing explained via alterations in the connectome in a whole-brain model

The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brain's connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the age differences in high-order functional interactions can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modelling more complex forms of pathological ageing or cognitive deficits.

A New Framework for Matching Forensic Composite Sketches With Digital Images

Face sketch recognition is considered as a sub-problem of face recognition. Matching composite sketches with its corresponding digital image is one of the challenging tasks. A new convolution neural network (CNN) framework for matching composite sketches with digital images is proposed in this work. The framework consists of a base CNN model that uses swish activation function in the hidden layers. Both composite sketches and digital images are trained separately in the network by providing matching pairs and mismatching pairs. The final output resulted from the network's final layer is compared with the threshold value, and then the pair is assigned to the same or different class. The proposed framework is evaluated on two datasets, and it exhibits an accuracy of 78.26% with extended-PRIP (E-PRIP) and 69.57% with composite sketches with age variations (CSA) respectively. Experimental analysis shows the improved results compared to state-of-the-art composite sketch matching systems.

CHALLENGES IN FACE RECOGNITION TECHNIQUE

Face detection is an application that is able of detecting, track, and recognizing human faces from an angle or video captured by a camera. A lot of advances have been made up in the domain of face recognition for security, identification, and appearance purpose, but still, difficult to able to beat humans alike accuracy. There are various problems in human facial presence such as; lighting conditions, image noise, scale, presentation, etc. Unconstrained face detection remains a difficult problem due to intra-class variations acquired by occlusion, disguise, capricious orientations, facial expressions, age variations…etc. The detection rate of face recognition algorithms is actually low in these conditions. With the popularity of AI in recent years, a mass number of enterprises deployed AI algorithms in absolute life settings. it is complete that face patterns observed by robots depend generally on variations such as pose, light environment, location.

Geographic and Age Variations in Low Body Mass Index Among Community-Dwelling Older People in Xinjiang: A Cross-Sectional Study

Background: Studies have shown an association between undernutrition and increased adverse outcome, as well as substantial geographic and age variations in undernutrition. Body mass index (BMI), a core indicator of undernutrition, is easy to measure and reflects the nutritional and health status of the human body. It is a simple and suitable tool for epidemiological investigations in large sample populations. Herein, we provide the first description of geographic and age variations in the prevalence of low BMI among community-dwelling older people in Xinjiang.Methods: From January 2019 to December 2019, using a multi-stage random sampling method, we conducted a cross-sectional epidemiological survey of the community-dwelling older people in Xinjiang at different latitudes. Of the 87,000 participants, the statistical analyses included 86,514 participants with complete data.Results: In Xinjiang, the prevalence of low BMI was 7.7% in the community-dwelling older people. The BMI gradually decreased with increasing age and gradually increased with latitude. The prevalence of low BMI in northern Xinjiang was 5.3%, which was significantly lower than that in eastern (7.7%) and southern (9.3%) Xinjiang. In the 60–69-, 70–79-, 80–89-, and ≥90-year age groups, the prevalence rates of low BMI were 5.8, 7.9, 10.0, and 13.9%, respectively. After adjusting for confounding factors (sex, ethnic group, hypertension, diabetes, hyperlipemia, smoking, and drinking), multivariate logistic regression analysis showed that the odds ratios (95% CI) for low BMI in eastern and southern Xinjiang were 1.165 (1.056–1.285) and 1.400 (1.274–1.538), respectively, compared to northern Xinjiang. The adjusted odds ratios (95% CI) for low BMI in the 70–79-, 80–89-, and ≥90-year age groups were 1.511 (1.39–1.635), 2.233 (2.030–2.456), and 3.003 (2.439–3.696), respectively, compared to the 60–69-year age group.Conclusion: The results of this study revealed geographic and age variations in the prevalence of low BMI in the community-dwelling older people in Xinjiang. The prevalence of low BMI gradually increased as the latitude decreased and as age increased.

Circadian Rhythms of 11-oxygenated C19 Steroids and ∆5-Steroid Sulfates in Healthy Men

Background: Many hormones display distinct circadian rhythms, driven by central regulators, hormonal bioavailability, and half-life. A set of 11-oxygenated C19 steroids (11-oxyandrogens) and pregnenolone sulfate (PregS) are elevated in congenital adrenal hyperplasia and other disorders, but their circadian patterns have not been characterized. Participants and methods: Peripheral blood was collected every 2 h over 24 h from healthy volunteer men (10 young, 18-30 years, and 10 older, 60-80 years). We used mass spectrometry to quantify 15 steroids, including: androstenedione (A4), testosterone (T), 11β-hydroxy- and 11-ketotestosterone (11OHT, 11KT),11β-hydroxy- and 11-ketoandrostenedione (11OHA4, 11KA4), and 4 ∆5-steroid sulfates. Diurnal models including: mesor (rhythm adjusted median), peak, and nadir concentrations, acrophase, and amplitude were computed. Results: 11OHA4 followed a rhythm similar to cortisol: acrophase, 8AM, nadir, 9PM and were similar in young and old men. 11KT had similar diurnal patterns, but the peak was lower in older than in young men, as was the case for A4. All 4 steroid sulfates were higher in young vs. older men. PregS and 17-hydroxypregnenolone sulfate (17OHPregS) showed sustained elevations between 8AM-6PM, and nadirs around midnight, while DHEAS and AdiolS displayed minimal diurnal variations. All 4 11-oxyandrogens correlated tightly with cortisol (r from 0.54 for 11OHT to 0.81 for 11OHA4, p<0.0001 for all), but very weakly with T, supporting their adrenal origin and ACTH governance. Conclusions: 11-oxyandrogens, PregS, and 17OHPregS display distinct circadian and age variations, which should be accounted for when used as clinical biomarkers.