About This OTS Dataset
Introduction
Welcome to the African Facial Expression Image Dataset, meticulously curated to enhance expression recognition models and support the development of advanced biometric identification systems, KYC models, and other facial recognition technologies.
Facial Expression Data
This dataset comprises over 2000 facial expression images, divided into participant-wise sets with each set including:
•
Expression Images:
5 different high-quality images per individual, each capturing a distinct facial emotion like Happy, Sad, Angry, Shocked, and Neutral.
Diversity and Representation
The dataset includes contributions from a diverse network of individuals across African countries, such as:
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Geographical Representation:
Participants from African countries, including Kenya, Malawi, Nigeria, Ethiopia, Benin, Somalia, Uganda, and more.
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Participant Profile:
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 facial expression image set is accompanied by detailed metadata for each participant, including:
•Participant Identifier
•File Name
•Age
•Gender
•Country
•Expression
•Demographic Information
•File Format
This metadata is essential for training models that can accurately recognize and identify expressions across different demographics and conditions.
Usage and Applications
This facial emotion dataset is ideal for various applications in the field of computer vision, including but not limited to:
•
Expression Recognition Models:
Improving the accuracy and reliability of facial expression 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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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 machine learning requirements. Therefore, we continuously add more assets with diverse conditions to this off-the-shelf facial expression 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.
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Custom Expression:
Any other expression can be included upon requirement.
License
This facial expression image dataset is created by FutureBeeAI and is available for commercial use.
Use Cases
Facial recognition
Expression Identification
Biometric Identification
Dataset Sample(s)
ATTRIBUTE
Gender | Age | Expression | Annotation |
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Female | 26 | Angry | NA |