Biometric Attack Dataset, Asian 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 asian people. 30,600+ photos & video of 15,300 people from 32 countries. All people presented in the dataset are South Asian, East Asian or Middle Asian. 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%2Ff545aa561432738d251c09f09e1f5e92%2FFrame%20104.png?generation=1713356643038606&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%2F6de78d350a9213d8437f766b085d4551%2Fasian_video_liveness.png?generation=1713356627116331&alt=media" alt="">

🧩 This is just an example of the data. Leave a request here to learn more

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

🚀 You can learn more about our high-quality unique datasets here

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, asian people, asian classification, asian image dataset

Biometric Attack Dataset, Asian 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 asian people. 30,600+ photos & video of 15,300 people from 32 countries. All people presented in the dataset are South Asian, East Asian or Middle Asian. The dataset helps in enchancing the performance of the model by providing wider range of data for a specific ethnic… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/asian-people-liveness-detection-video-dataset.

This report examines the rapid growth of the Asian American population in the U.S., highlighting key demographic trends, social challenges, and health-related needs. With Asian Americans now the fastest-growing minority group, reaching 5.6% of the total population, the study underscores the importance of addressing their evolving quality of life factors.

Introduction

Welcome to the East Asian Human Facial Images Dataset, curated to advance facial recognition technology and support the development of secure biometric identity systems, KYC verification processes, and AI-driven computer vision applications. This dataset is designed to serve as a robust foundation for real-world face matching and recognition use cases.

Facial Image Data

The dataset contains over 5,000 facial image sets of East Asian individuals. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

All images were captured with real-world variability to enhance dataset robustness:

Metadata

Each participant’s data is accompanied by rich metadata to support AI model training, including:

This metadata enables targeted filtering and training across diverse scenarios.

Use Cases & Applications

This dataset is ideal for a wide range of AI and biometric applications:

Secure & Ethical Collection

Dataset Updates & Customization

To meet evolving AI demands, this dataset is regularly updated and can be customized. Available options include:

Know Your Customer Dataset, Face Detection and Re-identification, Asian People

  The dataset is created on the basis of Selfies and ID Dataset

9,900+ photos including 1,300+ document photos from 660 people from 27 countries. The dataset includes 2 photos of a person from his documents and 13 selfies. All people presented in the dataset are South Asian, East Asian or Middle Asian. The dataset contains a variety of images capturing individuals from diverse backgrounds and… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/asian-kyc-photo-dataset.

The graph shows the results of a survey on whether superhero movies should have more characters that represent Asian people in the United States as of April 2019. During the survey, ** percent of the respondents stated that superhero movies should have more characters that represented Asian people.

Introduction

Welcome to the East Asian Multi-Year Facial Image Dataset, thoughtfully curated to support the development of advanced facial recognition systems, biometric identification models, KYC verification tools, and other computer vision applications. This dataset is ideal for training AI models to recognize individuals over time, track facial changes, and enhance age progression capabilities.

Facial Image Data

This dataset includes over 10,000+ high-quality facial images, organized into individual participant sets, each containing:

Diversity & Representation

Image Quality & Capture Conditions

To ensure model generalization and practical usability, images in this dataset reflect real-world diversity:

Metadata

Each participant’s dataset is accompanied by rich metadata to support advanced model training and analysis, including:

Use Cases & Applications

This dataset is highly valuable for a wide range of AI and computer vision applications:

Secure & Ethical Collection

Dataset Updates & Customization

To keep pace with evolving AI needs, this dataset is regularly updated and customizable. Custom data collection options include:

Facial Images Dataset of Asian & Non-Asian People.

Relative concentration of the Central California region's Asian American population. The variable ASIANALN records all individuals who select Asian as their SOLE racial identity in response to the Census questionnaire, regardless of their response to the Hispanic ethnicity question. Both Hispanic and non-Hispanic in the Census questionnaire are potentially associated with the Asian race alone.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as ASIANALN alone to the proportion of all people that live within the 4,961 block groups in the Central California RRK region that identify as ASIANALN alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of ASIANALN individuals compared to the Central California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then ASIANALN individuals are highly concentrated locally.

Historically there has been little information about children attending Region XI Head Start programs (programs operated by federally recognized tribes); however, in 2015 the first AIAN FACES study provided a national picture of children, families, and programs in Region XI. Native voices were at the forefront of this study in the AIAN FACES 2015 Workgroup, comprised of Region XI Head Start directors, researchers, and federal officials. AIAN FACES 2019 is the second round of this national study of Region XI Head Start children and families and their experiences in Head Start programs and classrooms. The AIAN FACES 2019 study design is the same as the design for AIAN FACES 2015. AIAN FACES 2019 convened its own workgroup with a composition similar to the 2015 workgroup. The AIAN FACES 2019 Workgroup provided advice on study activities from measurement updates to data collection and dissemination. AIAN FACES 2019 sought to (1) describe the strengths and needs of all children in Region XI, (2) provide an accurate picture of all children and families who participate in Region XI (AIAN and non-AIAN), and (3) understand the cultural and linguistic experiences of Native children and families in Region XI AIAN Head Start. Data collection with Region XI children, families, classrooms, and programs took place in the fall of 2019 and the spring of 2020. In both fall and spring, the study collected data from parent surveys and teacher child reports. In fall 2019, the study conducted direct child assessments. In spring 2020, teachers, center directors, and program directors completed surveys. Twenty-two Region XI Head Start programs participated. The study followed procedures for tribal review and approval in each of those 22 communities. AIAN FACES 2019 also planned to conduct direct child assessments and classroom observations in spring 2020. Due to the COVID-19 (for coronavirus disease 2019) pandemic, AIAN FACES cancelled in-person data collection (direct child assessments and classroom observations) after the second week of March, 2020. Therefore, the study was only able to collect direct child assessment and classroom observation data in seven of its 22 programs. For more information on the spring 2020 direct child assessments and classroom observations, see the Spring 2020 Partial Sample User's Manual. Researchers may request access to the Spring 2020 Partial Sample Data File containing these partial data from direct child assessments and classroom observations as part of their application. The data are provided in a separate file for exploratory purposes only. These partial data cannot be used to develop estimates representing Region XI children as a whole.

Graph and download economic data for Consumer Unit Characteristics: Percent White, Asian, and All Other Races, Not Including African American by Number of Earners: Consumer Units of Two or More People, Two Earners (CXUWHTNDOTHLB0706M) from 1984 to 2023 about asian, consumer unit, white, percent, persons, consumer, and USA.

About this dataset

Context

This list ranks the 1 cities in the Person County, NC by Multi-Racial Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Multi-Racial Asian Population: This column displays the rank of cities in the Person County, NC by their Multi-Racial Asian population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Multi-Racial Asian Population: The Multi-Racial Asian population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Multi-Racial Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Person County Multi-Racial Asian Population: This tells us how much of the entire Person County, NC Multi-Racial Asian population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Relative concentration of the Southern California region's Asian American population. The variable ASIANALN records all individuals who select Asian as their SOLE racial identity in response to the Census questionnaire, regardless of their response to the Hispanic ethnicity question. Both Hispanic and non-Hispanic in the Census questionnaire are potentially associated with the Asian race alone.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as ASIANALN alone to the proportion of all people that live within the 13,312 block groups in the Southern California RRK region that identify as ASIANALN alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of ASIANALN individuals compared to the Southern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then ASIANALN individuals are highly concentrated locally.

The belief that ethnic majorities dominate ethnic minorities informs research on intergroup processes. This belief can lead to the social heuristic that the ethnic majority sets an upper limit that minority groups cannot surpass, but this possibility has not received much attention. In three studies of perceived income, we examined how this heuristic, which we term the White ceiling heuristic leads people to inaccurately estimate the income of a minority group that surpasses the majority. We found that Asian Americans, whose median income has surpassed White median income for nearly three decades, are still perceived as making less than Whites, with the least accurate estimations being made by people who strongly believe that Whites are privileged. In contrast, income estimates for other minorities were fairly accurate. Thus, perceptions of minorities are shaped both by stereotype content and a heuristic.

This statistics shows the leading metropolitan areas in the United States in 2023 with the highest percentage of Asian population. Among the 81 largest metropolitan areas, Urban Honolulu, Hawaii was ranked first with **** percent of residents reporting as Asian in 2023.

The US Census Bureau defines American Indian or Alaskan Native as "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. This category includes people who indicate their race as "American Indian or Alaska Native" or report entries such as Navajo, Blackfeet, Inupiat, Yup'ik, or Central American Indian groups or South American Indian groups.Respondents who identified themselves as "American Indian or Alaska Native" were asked to report their enrolled or principal tribe. Therefore, tribal data in tabulations reflect the written entries reported on the questionnaires. Some of the entries (for example, Metlakatla Indian Community and Umatilla) represent reservations or a confederation of tribes on a reservation. The information on tribe is based on self-identification and, therefore, does not reflect any designation of federally or state-recognized tribe. The information for the 2010 Decennial Census was derived from the American Indian and Alaska Native Tribal Classification List for Decennial Census 2000 and updated from 2002 to 2009 based on the annual Federal Register notice entitled "Indian Entities Recognized and Eligible to Receive Services From the United States Bureau of Indian Affairs," Department of the Interior, Bureau of Indian Affairs, issued by OMB, and through consultation with American Indian and Alaska Native communities and leaders.". AIAN (American Indian / Alaskan Native) population percentage was calculated based upon total AIAN population within the census block group divided the total population of the same census block group. 2020 Census block groups for the Wichita / Sedgwick County area, clipped to the county line. Features were extracted from the 2020 State of Kansas Census Block Group shapefile provided by the State of Kansas GIS Data Access and Support Center (https://www.kansasgis.org/index.cfm).Change in Population and Housing for the Sedgwick County area from 2010 - 2020 based upon US Census. Census Blocks from 2010 were spatially joined to Census Block Groups from 2020 to compare the population and housing figures. This is not a product of the US Census Bureau and is only available through City of Wichita GIS. Please refer to Census Block Groups for 2010 and 2020 for verification of all data Standard block groups are clusters of blocks within the same census tract that have the same first digit of their 4-character census block number. For example, blocks 3001, 3002, 3003… 3999 in census tract 1210.02 belong to Block Group 3. Due to boundary and feature changes that occur throughout the decade, current block groups do not always maintain these same block number to block group relationships. For example, block 3001 might move due to a change in the census tract boundary. Even if the block is no longer in block group 3, the block number (3001) will not change. However, the identification string (GEOID20) for that block, identifying block group 3, would remain the same in the attribute information in the TIGER/Line Shapefiles because block identification strings are always built using the decennial geographic codes.Block groups delineated for the 2020 Census generally contain between 600 and 3,000 people. Local participants delineated most block groups as part of the Census Bureau's Participant Statistical Areas Program (PSAP). The Census Bureau delineated block groups only where a local or tribal government declined to participate or where the Census Bureau could not identify a potential local participant.A block group usually covers a contiguous area. Each census tract contains at least one block group and block groups are uniquely numbered within census tract. Within the standard census geographic hierarchy, block groups never cross county or census tract boundaries, but may cross the boundaries of county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian, Alaska Native, and Native Hawaiian areas.Block groups have a valid range of 0 through 9. Block groups beginning with a zero generally are in coastal and Great Lakes water and territorial seas. Rather than extending a census tract boundary into the Great Lakes or out to the 3-mile territorial sea limit, the Census Bureau delineated some census tract boundaries along the shoreline or just offshore.

The East Asian Social Survey (EASS) is a biennial social survey project that serves as a cross-national network of the following four General Social Survey type surveys in East Asia: the Chinese General Social Survey (CGSS), the Japanese General Social Survey (JGSS), the Korean General Social Survey (KGSS), and the Taiwan Social Change Survey (TSCS), and comparatively examines diverse aspects of social life in these regions. Since its 1st module survey in 2006, EASS produces and disseminates its module survey datasets and this is the harmonized data for the 7th module survey, called 'Culture and Globalization in East Asia'. Survey information in this module is the same topic as the second module of the EASS 2008, and it focuses on cultural norms and expectations of respondents. Respondents were asked about their exposure to East Asian cultural activities and rituals as well as opinion on family responsibilities and roles. Other topics include sources of international news and discussion frequency, countries or regions traveled, as well as where acquaintances live. Additionally, respondents were asked how accepting they would be of people from other countries as coworkers, neighbors, and in marriage. Information was collected regarding foreign practices, whether the respondent was working for a foreign capital company, and the economic environment. Respondents were also asked to assess their own proficiency when reading, speaking, and writing in English. Demographic information specific to the respondent and their spouse includes age, sex, marital status, education, employment status and hours worked, occupation, earnings and income, religion, class, size of community, and region.

Description This data includes Asian race, Caucasian race, black race, brown race and Indians. Each subject were collected 29 images under different scenes and light conditions. The 29 images include 28 photos (multi light conditions, multiple poses and multiple scenes) + 1 ID photo. This data can be used for face recognition related tasks. For more details, please visit: https://www.nexdata.ai/datasets/computervision/1016?source=Kaggle

Specifications

Data size 23,110 people, 29 images per person Race distribution 7,324 black people, 3,830 Caucasian people, 918 brown (Mexican) people, 6,270 Indian people and 4,768 Asian people Gender distribution 12,480 males , 10,630 females Age distribution ranging from teenager to the elderly, the middle-aged and young people are the majorities Collecting environment including indoor and outdoor scenes Data diversity different face poses, races, ages, light conditions and scenes Device cellphone Data format .jpg Accuracy the accuracy of labels of face pose, race, gender and age are more than 97%

Get the Dataset This is just an example of the data. To access more sample data or request the price for whole dataset, contact us at info@nexdata.ai

Graph and download economic data for Consumer Unit Characteristics: Number of People in CU by Race: Asian (CXU980010LB0904M) from 2003 to 2023 about asian, consumer unit, persons, and USA.

Graph and download economic data for Consumer Unit Characteristics: Age of Reference Person by Race: Asian (CXU980020LB0904M) from 2003 to 2023 about asian, consumer unit, age, personal, and USA.