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--- |
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license: cc-by-nc-nd-4.0 |
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task_categories: |
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- image-classification |
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- video-classification |
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- object-detection |
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tags: |
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- image |
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- video |
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- people |
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- computer vision |
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- deep learning |
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- cyber security |
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- verification |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Face Antispoofing dataset for recognition systems |
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The dataset consists of **98,000** videos and selfies from **170** countries, providing a foundation for developing robust **security systems** and **facial recognition algorithms.** |
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While the dataset itself doesn't contain spoofing attacks, it's a valuable resource for testing **liveness detection system**, allowing researchers to simulate attacks and evaluate how effectively their systems can distinguish between real faces and various forms of spoofing. |
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By utilizing this dataset, researchers can contribute to the development of advanced security solutions, enabling the safe and reliable use of biometric technologies for **authentication and verification**. - **[Get the data](https://unidata.pro/datasets/face-anti-spoofing/?utm_source=huggingface&utm_medium=cpc&utm_campaign=face-anti-spoofing)** |
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#Examples of data |
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The dataset offers a high-quality collection of videos and photos, including selfies taken with a range of popular smartphones, like iPhone, Xiaomi, Samsung, and more. The videos showcase individuals turning their heads in various directions, providing a natural range of movements for liveness detection training. |
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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=cpc&utm_campaign=face-anti-spoofing) to discuss your requirements and pricing options. |
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## Metadata for the dataset |
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Furthermore, the dataset provides detailed metadata for each set, including information like gender, age, ethnicity, video resolution, duration, and frames per second. This rich metadata provides crucial context for analysis and model development. |
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Researchers can develop more accurate liveness detection algorithms, which is crucial for achieving **the iBeta Level 2 certification**, a benchmark for robust and reliable biometric systems that prevent fraud. |
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# 🌐 [UniData](https://unidata.pro/datasets/face-anti-spoofing/?utm_source=huggingface&utm_medium=cpc&utm_campaign=face-anti-spoofing) provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects |
