Biometric Attack Dataset, Black People

The similar dataset that includes all ethnicities - Anti Spoofing Real Dataset

The dataset for face anti spoofing and face recognition includes images and videos of black people. The dataset helps in enchancing the performance of the model by providing wider range of data for a specific ethnic group.

The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users.

The dataset contains images and videos of real humans with various resolutions, views, and colors, making it a comprehensive resource for researchers working on anti-spoofing technologies.

People in the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F224e4e37b00a04546bbeaeded5fd3213%2FFrame%2095.png?generation=1712226592271540&alt=media" alt="">

Types of files in the dataset:

• photo - selfie of the person
• video - real video of the person

Our dataset also explores the use of neural architectures, such as deep neural networks, to facilitate the identification of distinguishing patterns and textures in different regions of the face, increasing the accuracy and generalizability of the anti-spoofing models.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• video_extension - video extensions in the dataset
• video_resolution - video resolution in the dataset
• video_duration - video duration in the dataset
• video_fps - frames per second for video in the dataset
• photo_extension - photo extensions in the dataset
• photo_resolution - photo resolution in the dataset

Statistics for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F9682c567213f0e6e99fecc3c6b511a9d%2FFrame%2096.png?generation=1712832044284031&alt=media" alt="">

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Content

The dataset consists of: - files - includes 10 folders corresponding to each person and including 1 image and 1 video, - .csv file - contains information about the files and people in the dataset

File with the extension .csv

• id: id of the person,
• selfie_link: link to access the photo,
• video_link: link to access the video,
• age: age of the person,
• country: country of the person,
• gender: gender of the person,
• video_extension: video extension,
• video_resolution: video resolution,
• video_duration: video duration,
• video_fps: frames per second for video,
• photo_extension: photo extension,
• photo_resolution: photo resolution

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F77905aea23afb7f61167bc9ccd0d98cb%2F7-ezgif.com-optimize.gif?generation=1707303271936246&alt=media" alt="">

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keywords: liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, ibeta dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset

The following data set is information obtained about counties in the United States from 2010 through 2019 through the United States Census Bureau. Information described in the data includes the age distributions, the education levels, employment statistics, ethnicity percents, houseold information, income, and other miscellneous statistics. (Values are denoted as -1, if the data is not available)

Data Dictionary

The percentage of persons, out of the total number of persons living in an area, self-identifying as racially Black or African American (and ethnically non-Hispanic). “Black or African American” refers to a person having origins in any of the Black racial groups of Africa. This indicator includes people who identified their race as “Black”. Source: U.S. Census Bureau, American Community Survey Years Available: 2010, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2020, 2017-2021, 2018-2022, 2019-2023

Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File. Definitions: Definitions of the Census Bureau’s categories are provided below. This interactive map shows patterns for all categories except American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. The total population countywide for these two categories is small (1,582 and 263 respectively). The Census Bureau uses the following race categories:Population by RaceWhite – A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.Black or African American – A person having origins in any of the Black racial groups of Africa.American Indian or Alaska Native – A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.Asian – A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.Native Hawaiian or Other Pacific Islander – A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.Some Other Race - this category is chosen by people who do not identify with any of the categories listed above. People can identify with more than one race. These people are included in the Two or More Races Hispanic or Latino PopulationThe Hispanic/Latino population is an ethnic group. Hispanic/Latino people may be of any race.Other layers provided in this tool included the Loudoun County Census block groups, towns and Dulles airport, and the Loudoun County 2021 aerial imagery.

Context

Percentage of resident persons who declared themselves black in relation to the total resident population, at the reference date of the Demographic Census. Source: IBGE, Demographic Census 2010 and Municipal fabric 2010. http://www.geoservicos.ibge.gov.br/geoserver/wms?service=WFS&version=1.0.0&request=GetFeature&typeName=CGEO:vw_per_black_people& om the dataset summary Population Census and Mesh ... License not specified spatial: "type": "Polygon", "coordinates": [[- [- 74.0046, -33.7411], [- 34.7929, -33.7411], [- 34.7929,5.2727], [- 74.0046,5.2727], [- 74.0046, -33.7411 ]]] http://dados.gov.br/dataset/cgeo_vw_per_pessoas_pretas

Content

Author and Maintainer: Geosciences Directorate - IBGE and Research Directorate - IBGE Last update: June 12, 2018 package id: 4565a7e3-9509-43dc-b074-433451ef7a47 Organ - Sphere: Federal. Organ - Power: Executive.

Acknowledgements

Geosciences Directorate - IBGE and Research Directorate - IBGE http://dados.gov.br

Photo by Anomaly on Unsplash

Inspiration

Nelson Mandela: was a South African anti-apartheid revolutionary, political leader, and philanthropist who served as President of South Africa from 1994 to 1999. He was the country's first black head of state and the first elected in a in a fully representative democratic election. His government focused on dismantling the legacy of apartheid by tackling institutionalized racism and fostering racial reconciliation. https://en.wikipedia.org/wiki/Nelson_Mandela

Martin Luther King Jr. (January 15, 1929 – April 4, 1968) was an American Christian minister and activist who became the most visible spokesperson and leader in the Civil Rights Movement from 1955 until his assassination in 1968. Born in Atlanta Georgia, King is best known for advancing civil rights through nonviolence and civil disobedience, inspired by his Christian beliefs and the nonviolent activism of Mahatma Gandhi. https://en.wikipedia.org/wiki/Martin_Luther_King_Jr.

BackgroundStudies on the barriers migrant women face when trying to access healthcare services in South Africa have emphasized economic factors, fear of deportation, lack of documentation, language barriers, xenophobia, and discrimination in society and in healthcare institutions as factors explaining migrants’ reluctance to seek healthcare. Our study aims to visualize some of the outcome effects of these barriers by analyzing data on maternal death and comparing the local population and black African migrant women from the South African Development Countries (SADC) living in South Africa. The heightened maternal mortality of black migrant women in South Africa can be associated with the hidden costs of barriers migrants face, including xenophobic attitudes experienced at public healthcare institutions.MethodsOur analysis is based on data on reported causes of death (COD) from the South African Department of Home Affairs (DHA). Statistics South Africa (Stats SA) processed the data further and coded the cause of death (COD) according to the WHO classification of disease, ICD10. The dataset is available on the StatsSA website (http://nesstar.statssa.gov.za:8282/webview/) for research and statistical purposes. The entire dataset consists of over 10 million records and about 50 variables of registered deaths that occurred in the country between 1997 and 2018. For our analysis, we have used data from 2002 to 2015, the years for which information on citizenship is reliably included on the death certificate. Corresponding benchmark data, in which nationality is recorded, exists only for a 10% sample from the population and housing census of 2011. Mid-year population estimates (MYPE) also exist but are not disaggregated by nationality. For this reason, certain estimates of death proportions by nationality will be relative and will not correspond to crude death rates.ResultsThe total number of female deaths recorded from the years 2002 to 2015 in the country was 3740.761. Of these, 99.09% (n = 3,707,003) were deaths of South Africans and 0.91% (n = 33,758) were deaths of SADC women citizens. For maternal mortality, we considered the total number of deaths recorded for women between the ages of 15 and 49 years of age and were 1,530,495 deaths. Of these, deaths due to pregnancy-related causes contributed to approximately 1% of deaths. South African women contributed to 17,228 maternal deaths and SADC women to 467 maternal deaths during the period under study. The odds ratio for this comparison was 2.02. In other words, our findings show the odds of a black migrant woman from a SADC country dying of a maternal death were more than twice that of a South African woman. This result is statistically significant as this odds ratio, 2.02, falls within the 95% confidence interval (1.82–2.22).ConclusionThe study is the first to examine and compare maternal death among two groups of women, women from SADC countries and South Africa, based on Stats SA data available for the years 2002–2015. This analysis allows for a better understanding of the differential impact that social determinants of health have on mortality among black migrant women in South Africa and considers access to healthcare as a determinant of health. As we examined maternal death, we inferred that the heightened mortality among black migrant women in South Africa was associated with various determinants of health, such as xenophobic attitudes of healthcare workers toward foreigners during the study period. The negative attitudes of healthcare workers toward migrants have been reported in the literature and the media. Yet, until now, its long-term impact on the health of the foreign population has not been gaged. While a direct association between the heightened death of migrant populations and xenophobia cannot be established in this study, we hope to offer evidence that supports the need to focus on the heightened vulnerability of black migrant women in South Africa. As we argued here, the heightened maternal mortality among migrant women can be considered hidden barriers in which health inequality and the pervasive effects of xenophobia perpetuate the health disparity of SADC migrants in South Africa.

South Africa is experiencing a rapidly growing diabetes epidemic that threatens its healthcare system. Research on the determinants of diabetes in South Africa receives considerable attention due to the lifestyle changes accompanying South Africa’s rapid urbanization since the fall of Apartheid. However, few studies have investigated how segments of the Black South African population, who continue to endure Apartheid’s institutional discriminatory legacy, experience this transition. This paper explores the association between individual and area-level socioeconomic status and diabetes prevalence, awareness, treatment, and control within a sample of Black South Africans aged 45 years or older in three municipalities in KwaZulu-Natal. Cross-sectional data were collected on 3,685 participants from February 2017 to February 2018. Individual-level socioeconomic status was assessed with employment status and educational attainment. Area-level deprivation was measured using the most recent South African Multidimensional Poverty Index scores. Covariates included age, sex, BMI, and hypertension diagnosis. The prevalence of diabetes was 23% (n = 830). Of those, 769 were aware of their diagnosis, 629 were receiving treatment, and 404 had their diabetes controlled. Compared to those with no formal education, Black South Africans with some high school education had increased diabetes prevalence, and those who had completed high school had lower prevalence of treatment receipt. Employment status was negatively associated with diabetes prevalence. Black South Africans living in more deprived wards had lower diabetes prevalence, and those residing in wards that became more deprived from 2001 to 2011 had a higher prevalence diabetes, as well as diabetic control. Results from this study can assist policymakers and practitioners in identifying modifiable risk factors for diabetes among Black South Africans to intervene on. Potential community-based interventions include those focused on patient empowerment and linkages to care. Such interventions should act in concert with policy changes, such as expanding the existing sugar-sweetened beverage tax.

In 1997 the Population Studies and Training Center (PSTC) of Brown University undertook a series of comparative training and research projects in three countries Vietnam, Ethiopia, and Guatemala. The projects were concerned with the training of planners and researchers in procedures for collecting and analyzing information on migration and its relation to development, women's status, health, and reproduction. Recognizing the importance of migration in South Africa and the pressing need for increasing the number of qualified researchers capable of focussing on this topic, in 1998 the Andrew W. Mellon Foundation provided additional funds to add South Africa to the project. The Centre for Population Studies (CENPOPS) at Pretoria University was given responsibility for the project, working in cooperation with scholars from PSTC at Brown University. The focus of the South African project was on the country's black population. Migration is defined in the survey as movement from one district to another or, if movement is within a district, between a rural and an urban area.

Abstract

Objective The objective of the study is to assess the burden and the spectrum of environmental and genetic determinants of T2D and selected associated microvascular complications in SSA. Methods A multi-national study integrating epidemiological and genomic techniques designed as case-series and population based cross-sectional surveys at 10 sites in 7 countries spanning west, east and southern Africa. Up to 6000 cases of T2D from health facilities and 6000 population based controls will be recruited. This design makes it possible to efficiently draw cases of T2D from health facilities and align them to controls from an appropriate base population while providing an opportunity to estimate prevalence from the survey component of the study. The integrated approach provides a framework for assessing burden, spectrum, and environmental and genetic risk factors for T2D and associated clinical complications.

Geographic coverage

Cameroon, Guinea, Malawi, Nigeria, South Africa, Tanzania, Uganda

Analysis unit

The unit of analysis is the human individual. Each record corresponds to an individual.

Universe

The population in both the survey and clinic arms of the study was of self-identified black Africans, 18 years or older and resident in their respective localities. The inclusion and exclusion criteria are in the table below.

Inclusion and Exclusion criteria

Clinic Arm Inclusion
Age=>25 years. Clinically diagnosed T2D based on data extracted from patient medical records according to current ADA and WHO definitions. Fasting plasma glucose (FPG) =>7.0mmol/ (=>126mg/dl) OR o Two-hours post-load glucose (2h-PG) =>11.1 mmol/l (=>200mg/dl) OR o Symptoms of diabetes and random plasma glucose => 11.1 mmol/l (=200mg/dl) OR o On oral or insulin treatment for diabetes. Individual of African origin (Black) Signed informed consent.

Exclusion · Pregnant women - can participate six months after childbirth · Diabetes classified other than T2D or doubt as to classification · Living outside the geographical sampling frame for the relevant site · Self-defined ethnic group regarded as other than African (Black) · Unable to give informed consent

Survey Arm Inclusion
Resident in the relevant geographical sampling frame Age=>18 years · Individual of African origin (Black) Signed informed consent

Exclusion · Pregnant women - can participate six months after childbirth · Resident outside the relevant geographical sampling frame · Self-defined ethnic group regarded as other than African (Black) · Unable to give informed consent

Kind of data

Includes data on:Socio-demographic, biophysical and anthropometric, biochemical factors as well as fundus image grading.

Sampling procedure

T2D cases were recruited purposively selected from health facilities within the geographical location of study centres using patient registers as sampling frames. Surveys were conducted in the catchment areas of the selected health facilities using a two-stage cluster sampling design involving predefined geographical areas such as administrative units and households. Listings of administrative units and households were obtained from each country's National Statistics Office or equivalent agency, or generated with the help of a local leader of the area to provide a sampling frame.

HLA Class II Haplotype Frequency Distributions (for 99% haplotypes per population) and HLA Class II Simulated Populations (Genotype level information for sample sizes of 1000, 5000, 10000 simulated individuals) for 4 broad and 21 detailed US population groups.

Broad population groups: African Americans (AFA), Asian and Pacific Islanders (API), Caucasians (CAU), Hispanics (HIS).

Detailed population groups: African American (AAFA), African (AFB), South Asian Indian (AINDI), American Indian - South or Central American (AISC), Alaska native of Aleut (ALANAM), North American Indian (AMIND), Caribbean Black (CARB), Caribbean Hispanic (CARHIS), Caribbean Indian (CARIBI), European Caucasian (EURCAU), Filipino (FILII), Hawaiian or other Pacific Islander (HAWI), Japanese (JAPI), Korean (KORI), Middle Eastern or North Coast of Africa (MENAFC), Mexican or Chicano (MSWHIS), Chinese (NCHI), Hispanic - South or Central American (SCAHIS), Black - South or Central American (SCAMB), Southeast Asian (SCSEAI), Vietnamese (VIET).

Colonization records include documents from two government agencies that raised money and support for the removal of formerly enslaved people to Liberia. As early as 1691, the Virginia General Assembly began passing laws that forced free Black Virginians to leave the Commonwealth. Fears around insurrection and the desire to control the Black population gave rise to institutions dedicated to removing free people of color from Virginia.

The General Assembly passed an act in 1833 "making appropriations for the removal of free persons of color" to the western coast of Africa and established a board of commissioners charged with carrying out the provisions of the act. “The Board of Commissioners for the Removal of Free Persons of Color records, 1833-1856,” contain correspondence, lists, minutes, oaths, and resolutions. Included are lists of free Black individuals who emigrated to Liberia (including the name of the ship), lists of free Black individuals willing to emigrate, and resolutions to send money to the American Colonization Society and to those who transported the free Black people to Liberia. Also included is a report of the Board of Commissioners, 1835, containing a list of free Black people transported to Liberia and including their names, ages, and where they had lived in Virginia.

The General Assembly passed an act on April 6, 1853 to create the Colonization Board of Virginia, (chap. 55, p. 58). This act also created appropriations to fund the voluntary transportation and removal of free Black individuals to Liberia or elsewhere in West Africa through the efforts of the Virginia branch of the American Colonization Society. Statutory members of the board included the Secretary of the Commonwealth, the Auditor of Public Accounts, the Second Auditor of Public Accounts, and four other competent members appointed by the Governor. An annual tax was levied on free Black men between the ages of 21 to 55 to help finance the operations of the board. The Colonization Board was authorized to reimburse the agents of the Virginia Colonization Society for transportation costs only after receiving satisfactory proof that the formerly enslaved individuals had been transported out of the state. The Virginia Colonization Society arranged for the actual passage of free Black individuals, and at each meeting the Board received affidavits for particular free people who had already been transported, along with evidence that the individuals were free or born of free parents, that they were residents of Virginia and that they had already been transported to Africa or that they had embarked to another state for transportation. The Board was required to keep a journal of its proceedings, showing all actions taken and monies disbursed, and was also required to submit a biennial report to the General Assembly showing the name, age, sex, and locality of each person removed. The board held its last meeting on August 14, 1858, after the preceding session of the General Assembly failed to extend its existence. The Virginia Board of Colonization journal of proceedings includes lists of the names and ages of free Black individuals transported from the commonwealth to Africa, as well as the county, city, or borough from which they were transported, and in some instances also includes the name of the ship and names of former enslavers.

Data in this collection is drawn directly from the original historical records and may contain terminology which is now deemed offensive.

The dataset provides the predominate and traditional family names of African Nova Scotians in 6 regions in Nova Scotia. The regions consist of Halifax Metro, South Shore and Yarmouth and Acadian Shore, Bay of Fundy and Annapolis Valley, Northumberland Shore, Eastern Shore and Cape Breton Island. Within all these regions you find 48+ traditional African Nova Scotian communities. The dataset will also provide the communities you can find in each of the six regions.

Effect of demographic parameters on ascribed aetiology of CKD.