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East Asian Children Facial Image Dataset for Facial Recognition
- futurebeeai.com
wavUpdated Aug 1, 2022+ more versions
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EmailClick to copy linkLink copiedCiteFutureBee AI (2022). East Asian Children Facial Image Dataset for Facial Recognition [Dataset]. https://www.futurebeeai.com/dataset/image-dataset/facial-images-minor-east-asianwavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Area coveredEast AsiaDataset funded byFutureBeeAIDescriptionIntroduction
The East Asian Children Facial Image Dataset is a thoughtfully curated collection designed to support the development of advanced facial recognition systems, biometric identity verification, age estimation tools, and child-specific AI models. This dataset enables researchers and developers to build highly accurate, inclusive, and ethically sourced AI solutions for real-world applications.
Facial Image Data
The dataset includes over 1500 high-resolution image sets of children under the age of 18. Each participant contributes approximately 15 unique facial images, captured to reflect natural variations in appearance and context.
Diversity and Representation
•Geographic Coverage: Children from China, Japan, Philippines, Malaysia, Singapore, Thailand, Vietnam, Indonesia, and more•Age Group: All participants are minors, with a wide age spread across childhood and adolescence.•Gender Balance: Includes both boys and girls, representing a balanced gender distribution.•File Formats: Images are available in JPEG and HEIC formats.Quality and Image Conditions
To ensure robust model training and generalizability, images are captured under varied natural conditions:
•Lighting: A mix of lighting setups, including indoor, outdoor, bright, and low-light scenarios.•Backgrounds: Diverse backgrounds—plain, natural, and everyday environments—are included to promote realism.•Capture Devices: All photos are taken using modern mobile devices, ensuring high resolution and sharp detail.Metadata
Each child’s image set is paired with detailed, structured metadata, enabling granular control and filtering during model training:
•Unique Participant ID•File Name•Age•Gender•Country•Demographic Attributes•File FormatThis metadata is essential for applications that require demographic awareness, such as region-specific facial recognition or bias mitigation in AI models.
Applications
This dataset is ideal for a wide range of computer vision use cases, including:
•Facial Recognition: Improving identification accuracy across diverse child demographics.•KYC and Identity Verification: Enabling more inclusive onboarding processes for child-specific platforms.•Biometric Systems: Supporting child-focused identity verification in education, healthcare, or travel.•Age Estimation: Training AI models to estimate age ranges of children from facial features.•Child Safety Models: Assisting in missing child identification or online content moderation.•Generative AI Training: Creating more representative synthetic data using real-world diverse inputs.Ethical Collection and Data Security
We maintain the highest ethical and security standards throughout the data lifecycle:
•Guardian Consent: Every participant’s guardian provided informed, written consent, clearly outlining the dataset’s use cases.•Privacy-First Approach: Personally identifiable information is not shared. Only anonymized metadata is included.•Secure Storage: QS World University Rankings 2024
- kaggle.com
Updated Jul 31, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteJoakim Arvidsson (2023). QS World University Rankings 2024 [Dataset]. https://www.kaggle.com/datasets/joebeachcapital/qs-world-university-rankings-2024/codeCroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.Dataset updatedJul 31, 2023Dataset provided byKaggleAuthorsJoakim ArvidssonLicenseAttribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
License information was derived automaticallyDescriptionQS World University Rankings 2024: Top global universities
The 20th edition of the QS World University Rankings features 1,500 institutions across 104 locations and is the only ranking of its kind to emphasise employability and sustainability. This year, they have implemented the largest-ever methodological enhancement, introducing three new metrics: Sustainability, Employment Outcomes and International Research Network.
The results draw on the analysis of 17.5m academic papers and the expert opinions of over 240,000 academic faculty and employers. Massachusetts Institute of Technology celebrates twelve years at the top, the University of Cambridge retains 2nd place while the University of Oxford (3rd) climbs one position.
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African Children Facial Image Dataset for Facial Recognition
- futurebeeai.com
wavUpdated Aug 1, 2022ShareFacebookTwitterEmailClick to copy linkLink copiedCiteFutureBee AI (2022). African Children Facial Image Dataset for Facial Recognition [Dataset]. https://www.futurebeeai.com/dataset/image-dataset/facial-images-minor-africanwavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Dataset funded byFutureBeeAIDescriptionIntroduction
The African Children Facial Image Dataset is a thoughtfully curated collection designed to support the development of advanced facial recognition systems, biometric identity verification, age estimation tools, and child-specific AI models. This dataset enables researchers and developers to build highly accurate, inclusive, and ethically sourced AI solutions for real-world applications.
Facial Image Data
The dataset includes over 1500 high-resolution image sets of children under the age of 18. Each participant contributes approximately 15 unique facial images, captured to reflect natural variations in appearance and context.
Diversity and Representation
•Geographic Coverage: Children from Kenya, Malawi, Nigeria, Ethiopia, Benin, Somalia, Uganda, and more•Age Group: All participants are minors, with a wide age spread across childhood and adolescence.•Gender Balance: Includes both boys and girls, representing a balanced gender distribution.•File Formats: Images are available in JPEG and HEIC formats.Quality and Image Conditions
To ensure robust model training and generalizability, images are captured under varied natural conditions:
•Lighting: A mix of lighting setups, including indoor, outdoor, bright, and low-light scenarios.•Backgrounds: Diverse backgrounds—plain, natural, and everyday environments—are included to promote realism.•Capture Devices: All photos are taken using modern mobile devices, ensuring high resolution and sharp detail.Metadata
Each child’s image set is paired with detailed, structured metadata, enabling granular control and filtering during model training:
•Unique Participant ID•File Name•Age•Gender•Country•Demographic Attributes•File FormatThis metadata is essential for applications that require demographic awareness, such as region-specific facial recognition or bias mitigation in AI models.
Applications
This dataset is ideal for a wide range of computer vision use cases, including:
•Facial Recognition: Improving identification accuracy across diverse child demographics.•KYC and Identity Verification: Enabling more inclusive onboarding processes for child-specific platforms.•Biometric Systems: Supporting child-focused identity verification in education, healthcare, or travel.•Age Estimation: Training AI models to estimate age ranges of children from facial features.•Child Safety Models: Assisting in missing child identification or online content moderation.•Generative AI Training: Creating more representative synthetic data using real-world diverse inputs.Ethical Collection and Data Security
We maintain the highest ethical and security standards throughout the data lifecycle:
•Guardian Consent: Every participant’s guardian provided informed, written consent, clearly outlining the dataset’s use cases.•Privacy-First Approach: Personally identifiable information is not shared. Only anonymized metadata is included.•Secure Storage: <span - F
South Asian Children Facial Image Dataset for Facial Recognition
- futurebeeai.com
wavUpdated Aug 1, 2022ShareFacebookTwitterEmailClick to copy linkLink copiedCiteFutureBee AI (2022). South Asian Children Facial Image Dataset for Facial Recognition [Dataset]. https://www.futurebeeai.com/dataset/image-dataset/facial-images-minor-south-asianwavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Area coveredSouth AsiaDataset funded byFutureBeeAIDescriptionIntroduction
The South Asian Children Facial Image Dataset is a thoughtfully curated collection designed to support the development of advanced facial recognition systems, biometric identity verification, age estimation tools, and child-specific AI models. This dataset enables researchers and developers to build highly accurate, inclusive, and ethically sourced AI solutions for real-world applications.
Facial Image Data
The dataset includes over 1500 high-resolution image sets of children under the age of 18. Each participant contributes approximately 15 unique facial images, captured to reflect natural variations in appearance and context.
Diversity and Representation
•Geographic Coverage: Children from India, Pakistan, Bangladesh, Nepal, Sri Lanka, Bhutan, Maldives, and more•Age Group: All participants are minors, with a wide age spread across childhood and adolescence.•Gender Balance: Includes both boys and girls, representing a balanced gender distribution.•File Formats: Images are available in JPEG and HEIC formats.Quality and Image Conditions
To ensure robust model training and generalizability, images are captured under varied natural conditions:
•Lighting: A mix of lighting setups, including indoor, outdoor, bright, and low-light scenarios.•Backgrounds: Diverse backgrounds—plain, natural, and everyday environments—are included to promote realism.•Capture Devices: All photos are taken using modern mobile devices, ensuring high resolution and sharp detail.Metadata
Each child’s image set is paired with detailed, structured metadata, enabling granular control and filtering during model training:
•Unique Participant ID•File Name•Age•Gender•Country•Demographic Attributes•File FormatThis metadata is essential for applications that require demographic awareness, such as region-specific facial recognition or bias mitigation in AI models.
Applications
This dataset is ideal for a wide range of computer vision use cases, including:
•Facial Recognition: Improving identification accuracy across diverse child demographics.•KYC and Identity Verification: Enabling more inclusive onboarding processes for child-specific platforms.•Biometric Systems: Supporting child-focused identity verification in education, healthcare, or travel.•Age Estimation: Training AI models to estimate age ranges of children from facial features.•Child Safety Models: Assisting in missing child identification or online content moderation.•Generative AI Training: Creating more representative synthetic data using real-world diverse inputs.Ethical Collection and Data Security
We maintain the highest ethical and security standards throughout the data lifecycle:
•Guardian Consent: Every participant’s guardian provided informed, written consent, clearly outlining the dataset’s use cases.•Privacy-First Approach: Personally identifiable information is not shared. Only anonymized metadata is included.•Secure Storage: <span 
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Introduction
The East Asian Children Facial Image Dataset is a thoughtfully curated collection designed to support the development of advanced facial recognition systems, biometric identity verification, age estimation tools, and child-specific AI models. This dataset enables researchers and developers to build highly accurate, inclusive, and ethically sourced AI solutions for real-world applications.
Facial Image Data
The dataset includes over 1500 high-resolution image sets of children under the age of 18. Each participant contributes approximately 15 unique facial images, captured to reflect natural variations in appearance and context.
Diversity and Representation
Quality and Image Conditions
To ensure robust model training and generalizability, images are captured under varied natural conditions:
Metadata
Each child’s image set is paired with detailed, structured metadata, enabling granular control and filtering during model training:
This metadata is essential for applications that require demographic awareness, such as region-specific facial recognition or bias mitigation in AI models.
Applications
This dataset is ideal for a wide range of computer vision use cases, including:
Ethical Collection and Data Security
We maintain the highest ethical and security standards throughout the data lifecycle: