Update README.md

README.md

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-license: cc-by-nc-nd-4.0
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+---
+license: cc-by-nc-nd-4.0
+task_categories:
+- video-classification
+tags:
+- people
+- mask
+- computer vison
+- facial recognition
+- verification
+- cyber security
+size_categories:
+- 10K<n<100K
+---
+# 2D Masks with Eyeholes Attacks
+The dataset comprises **11,200+** videos of people wearing of holding 2D printed masks with eyeholes captured using **5** different devices. This extensive collection is designed for research in presentation attacks, focusing on various **detection methods**, primarily aimed at meeting the requirements for **iBeta Level 1 & 2 certification.** Specifically engineered to challenge **facial recognition** and enhance **spoofing detection** techniques.
+
+By utilizing this dataset, researchers and developers can advance their understanding and capabilities in **biometric security** and **liveness detection technologies**. - **[Get the data](https://unidata.pro/datasets/2d-masks/?utm_source=huggingface&utm_medium=cpc&utm_campaign=2d-masks-pad-attacks)**
+## Attacks in the dataset
+![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F589023f5a5202dd588e5d9df56747037%2FFrame%20166.png?generation=1731498279480466&alt=media)
+The attacks were recorded in various settings, showcasing individuals with different attributes. Each photograph features human faces adorned with 2D masks, simulating potential spoofing attempts in facial recognition systems.
+
+**Variants of backgrounds and attributes in the dataset**:
+![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2Fbf3ed70a8a519ebb4bbdd8fc634bd4f3%2FFrame%20146%20(2).png?generation=1730208154622175&alt=media)
+# 💵 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/2d-masks/?utm_source=huggingface&utm_medium=cpc&utm_campaign=2d-masks-pad-attacks) to discuss your requirements and pricing options.
+Researchers can utilize this dataset to explore detection technology and recognition algorithms that aim to prevent impostor attacks and improve authentication processes.
+## Metadata for the dataset
+![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F599c49641aa40b28eef7684a086fb98b%2FFrame%20161%20(4).png?generation=1731498379460267&alt=media)
+**Variables in .csv files:**
+
+-   **name:** filename of the printed 2D mask
+-   **path:** link-path for the original video
+-   **type:** type(wearing or holding) of printed mask
+
+The dataset provides a robust foundation for achieving higher detection accuracy and advancing liveness detection methods, which are essential for preventing identity fraud and ensuring reliable biometric verification.
+
+# 🌐 [UniData](https://unidata.pro/datasets/2d-masks/utm_source=huggingface&utm_medium=cpc&utm_campaign=2d-masks-pad-attacks) provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects