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README.md

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 ## Metadata for the dataset
 ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F87acb75b060abcd7838e8a9fad21fb79%2FFrame%201%20(8).png?generation=1743153407873743&alt=media)
 All images come with rigorously verified metadata annotations (age, gender, ethnicity), achieving ≥95% labeling accuracy. Also images are captured under different lighting conditions and resolutions, enhancing the dataset's utility for computer vision tasks and image classifications.
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 # 💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at [https://unidata.pro](https://unidata.pro/datasets/face-anti-spoofing/?utm_source=huggingface&utm_medium=referral&utm_campaign=face-recognition-image-dataset) to discuss your requirements and pricing options.
 
 Researchers can leverage this dataset to improve recognition technology and develop learning models that enhance the accuracy of face detections. The dataset also supports projects focused on face anti-spoofing and deep learning applications, making it an essential tool for those studying biometric security and liveness detection technologies.
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+# Frequently Asked Questions
+
+## How do paired images and videos improve biometric model development?
+
+Each participant is represented by both a still image and a corresponding live video, allowing researchers to combine static facial feature learning with temporal liveness analysis.
+
+## Who can benefit from this face recognition image dataset?
+
+This dataset is useful for biometric authentication providers, AI researchers, computer vision engineers, fintech companies, and academic institutions developing facial recognition technologies. Since it includes paired images and live videos, it supports both identity verification and face anti spoofing research. The combination of demographic metadata and real capture conditions also makes the dataset valuable for benchmarking recognition systems, evaluating fairness, and improving biometric security across diverse deployment environments.
+
+## How was the dataset collected?
+
+The dataset was gathered through crowdsourcing platforms using a standardized acquisition process designed to capture real participants under practical conditions.
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 # 🌐 [UniData](https://unidata.pro/datasets/face-anti-spoofing/?utm_source=huggingface&utm_medium=referral&utm_campaign=face-recognition-image-dataset) provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects