Datasets:

UniqueData
/

biometric-attacks-in-different-lighting-conditions

@@ -5,14 +5,19 @@ task_categories:
 language:
 - en
 tags:
-- code
-- legal
-- finance
 ---
 # Biometric Attack Dataset - Different Lighting Conditions Dataset
 The liveness detection dataset consists of videos of individuals and attacks with photos shown in the monitor . Videos are filmed in different lightning conditions (*in a dark room, daylight, light room and nightlight*) and in different places (*indoors, outdoors*). Each video in the dataset has an approximate duration of 20 seconds.
-# 💴 For Commercial Usage: Full version of the dataset includes 7296 videos, leave a request on **[TrainingData](https://trainingdata.pro/datasets?utm_source=huggingface&utm_medium=cpc&utm_campaign=biometric-attacks-in-different-lighting-conditions)** to buy the dataset
 ### Types of videos in the dataset:
 - **darkroom_photo** - photo of a person in a **dark room** shown on a computer and filmed on the phone
@@ -33,7 +38,7 @@ The dataset comprises videos of genuine facial presentations using various metho
 Our results show that this technology works effectively in securing most applications and prevents unauthorized access by distinguishing between genuine and spoofed inputs. Additionally, it addresses the challenging task of identifying unseen spoofing cues, making it one of the most effective techniques in the field of anti-spoofing research.
-# 💴 Buy the Dataset: This is just an example of the data. Leave a request on **[https://trainingdata.pro/datasets](https://trainingdata.pro/datasets?utm_source=huggingface&utm_medium=cpc&utm_campaign=biometric-attacks-in-different-lighting-conditions) to discuss your requirements, learn about the price and buy the dataset**
 # Content
 - **files** - contains of original videos and videos of attacks,
@@ -43,10 +48,6 @@ Our results show that this technology works effectively in securing most applica
 - **file**: link to the video,
 - **type**: type of the video
-## **[TrainingData](https://trainingdata.pro/datasets?utm_source=huggingface&utm_medium=cpc&utm_campaign=biometric-attacks-in-different-lighting-conditions)** provides high-quality data annotation tailored to your needs
-More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
-TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
 *keywords: ibeta level 1, ibeta level 2, , video replay attack, replay attack dataset, replay attack database, replay mobile dataset, video attack attempts, liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, face recognition, face detection, face identification, human video dataset, video dataset, presentation attack detection, presentation attack dataset, 2d print attacks, print 2d attacks dataset, phone attack dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset, cut prints spoof attack*

 language:
 - en
 tags:
+- ibeta
+- replay attack
+- video
+- liveness detection
+- biometric
+- anti-spoofing
+size_categories:
+- 10K<n<100K
 ---
 # Biometric Attack Dataset - Different Lighting Conditions Dataset
 The liveness detection dataset consists of videos of individuals and attacks with photos shown in the monitor . Videos are filmed in different lightning conditions (*in a dark room, daylight, light room and nightlight*) and in different places (*indoors, outdoors*). Each video in the dataset has an approximate duration of 20 seconds.
+# The dataset is created on the basis of [iBeta Level 1 Dataset](https://unidata.pro/datasets/ibeta-level-1-video-attacks/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=biometric-attacks-in-different-lighting)
 ### Types of videos in the dataset:
 - **darkroom_photo** - photo of a person in a **dark room** shown on a computer and filmed on the phone
 Our results show that this technology works effectively in securing most applications and prevents unauthorized access by distinguishing between genuine and spoofed inputs. Additionally, it addresses the challenging task of identifying unseen spoofing cues, making it one of the most effective techniques in the field of anti-spoofing research.
+##  👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 25,000+ human images & videos - [Full dataset](https://unidata.pro/datasets/ibeta-level-1-video-attacks/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=biometric-attacks-in-different-lighting)
 # Content
 - **files** - contains of original videos and videos of attacks,
 - **file**: link to the video,
 - **type**: type of the video
+#**🚀 You can learn more about our high-quality unique datasets [here](https://unidata.pro/datasets/ibeta-level-1-video-attacks/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=biometric-attacks-in-different-lighting)**
 *keywords: ibeta level 1, ibeta level 2, , video replay attack, replay attack dataset, replay attack database, replay mobile dataset, video attack attempts, liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, face recognition, face detection, face identification, human video dataset, video dataset, presentation attack detection, presentation attack dataset, 2d print attacks, print 2d attacks dataset, phone attack dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset, cut prints spoof attack*