About This OTS Dataset
Introduction
Welcome to the African Facial Images from Past Dataset, meticulously curated to enhance face recognition models and support the development of advanced biometric identification systems, KYC models, and other facial recognition technologies.
Facial Image Data
This dataset comprises over 10,000+ images, divided into participant-wise sets with each set including:
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Historical Images:
22 different high-quality historical images per individual from the timeline of 10 years.
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Enrollment Image:
One modern high-quality image for reference.
Diversity and Representation
The dataset includes contributions from a diverse network of individuals across African countries:
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Geographical Representation:
Participants from countries including Kenya, Malawi, Nigeria, Ethiopia, Benin, Somalia, Uganda, and more.
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Demographics:
Participants range from 18 to 70 years old, representing both males and females in 60:40 ratio, respectively.
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File Format:
The dataset contains images in JPEG and HEIC file format.
Quality and Conditions
To ensure high utility and robustness, all images are captured under varying conditions:
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Lighting Conditions:
Images are taken in different lighting environments to ensure variability and realism.
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Backgrounds:
A variety of backgrounds are available to enhance model generalization.
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Device Quality:
Photos are taken using the latest mobile devices to ensure high resolution and clarity.
Metadata
Each image set is accompanied by detailed metadata for each participant, including:
•Participant Identifier
•File Name
•Age at the time of capture
•Gender
•Country
•Demographic Information
•File Format
This metadata is essential for training models that can accurately recognize and identify African faces across different demographics and conditions.
Usage and Applications
This facial image dataset is ideal for various applications in the field of computer vision, including but not limited to:
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Facial Recognition Models:
Improving the accuracy and reliability of facial recognition systems.
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KYC Models:
Streamlining the identity verification processes for financial and other services.
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Biometric Identity Systems:
Developing robust biometric identification solutions.
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Age Prediction Models:
Training models to accurately predict the age of individuals based on facial features.
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Generative AI Models:
Training generative AI models to create realistic and diverse synthetic facial images.
Secure and Ethical Collection
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Data Security:
Data was securely stored and processed within our platform, ensuring data security and confidentiality.
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Ethical Guidelines:
The biometric data collection process adhered to strict ethical guidelines, ensuring the privacy and consent of all participants.
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Participant Consent:
All participants were informed of the purpose of collection and potential use of the data, as agreed through written consent.
Updates and Customization
We understand the evolving nature of AI and facial biometric model requirements. Therefore, we continuously add more assets with diverse conditions to this off-the-shelf facial image dataset.
•Customization & Custom Collection Options:
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Background Conditions:
Specific conditions upon request, like indoor or outdoor.
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Lighting Condition:
Different lighting conditions can be achieved.
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Capture Time:
Variation can be achieved by capturing images at different times of day like morning, afternoon, evening, or night as per requirement.
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Resolution:
Custom collection as per requirement.
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Annotation:
Custom annotations like facial landmarks, facial boundaries, semantics, or any other application-specific annotations can be done upon request.
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Device-specific Collection:
Data can be collected from specific devices with specific brands or operating systems.
License
This facial image training dataset is created by FutureBeeAI and is available for commercial use.
Use Cases
Facial recognition
Security & Surveillance
Dataset Sample(s)
FILE DETAILS
These samples are to give you a glimpse of the actual facial dataset. The actual dataset is diverse across different age groups, genders, backgrounds, and lighting conditions to help you build a robust and unbiased facial recognition AI model.
ATTRIBUTE
Gender | Age | Accessory | Annotation |
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Female | 20 | NA | NA |