Update README.md
Browse files
README.md
CHANGED
@@ -20,7 +20,7 @@ size_categories:
|
|
20 |
|
21 |
The dataset contains **6,500+** videos of attacks from **50** different people, filmed using **5 devices**, providing a valuable resource for researching presentation attacks in **facial recognition technologies**. By focusing on this area, the dataset facilitates experiments designed to improve **biometric security** and **anti-spoofing** measures, ultimately aiding in the creation of more robust and reliable authentication systems.
|
22 |
|
23 |
-
By utilizing this dataset, 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. - **[Get the data](https://unidata.pro/datasets/silicone-mask-attacks/?utm_source=huggingface&utm_medium=
|
24 |
|
25 |
## Attacks in the dataset
|
26 |
.png?generation=1730202854031131&alt=media)
|
@@ -29,11 +29,11 @@ The attacks were recorded in diverse settings, showcasing individuals with vario
|
|
29 |
**Variants of backgrounds and attributes in the dataset**:
|
30 |
.png?generation=1730208154622175&alt=media)
|
31 |
|
32 |
-
# 💵 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/silicone-mask-attacks/?utm_source=huggingface&utm_medium=
|
33 |
|
34 |
## Metadata for the dataset
|
35 |
.png?generation=1730156029345280&alt=media)
|
36 |
|
37 |
Researchers can learn more about the performance of recognition systems by examining this dataset, which reveals insights into the vulnerabilities of security systems. This data can help improve liveness detection systems, which are independently certified by iBeta, an independent laboratory that assesses the reliability of these systems.
|
38 |
|
39 |
-
# 🌐 [UniData](https://unidata.pro/datasets/silicone-mask-attacks/?utm_source=huggingface&utm_medium=
|
|
|
20 |
|
21 |
The dataset contains **6,500+** videos of attacks from **50** different people, filmed using **5 devices**, providing a valuable resource for researching presentation attacks in **facial recognition technologies**. By focusing on this area, the dataset facilitates experiments designed to improve **biometric security** and **anti-spoofing** measures, ultimately aiding in the creation of more robust and reliable authentication systems.
|
22 |
|
23 |
+
By utilizing this dataset, 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. - **[Get the data](https://unidata.pro/datasets/silicone-mask-attacks/?utm_source=huggingface&utm_medium=referral&utm_campaign=silicone-mask-attack)**
|
24 |
|
25 |
## Attacks in the dataset
|
26 |
.png?generation=1730202854031131&alt=media)
|
|
|
29 |
**Variants of backgrounds and attributes in the dataset**:
|
30 |
.png?generation=1730208154622175&alt=media)
|
31 |
|
32 |
+
# 💵 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/silicone-mask-attacks/?utm_source=huggingface&utm_medium=referral&utm_campaign=silicone-mask-attack) to discuss your requirements and pricing options.
|
33 |
|
34 |
## Metadata for the dataset
|
35 |
.png?generation=1730156029345280&alt=media)
|
36 |
|
37 |
Researchers can learn more about the performance of recognition systems by examining this dataset, which reveals insights into the vulnerabilities of security systems. This data can help improve liveness detection systems, which are independently certified by iBeta, an independent laboratory that assesses the reliability of these systems.
|
38 |
|
39 |
+
# 🌐 [UniData](https://unidata.pro/datasets/silicone-mask-attacks/?utm_source=huggingface&utm_medium=referral&utm_campaign=silicone-mask-attack) provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects
|