TrainingDataPro/biometric-attacks-in-different-lighting-conditions

---
license: cc-by-nc-nd-4.0
task_categories:
- video-classification
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
- **daylight_photo** - photo of a person in a **daylight** shown on a computer and filmed on the phone
- **lightroom_photo** - photo of a person in a **light room** shown on a computer and filmed on the phone
- **nightlight_photo** - photo of a person in a **night light** shown on a computer and filmed on the phone
- **darkroom_video** - filmed in a **dark room**, on which a person moves his/her head left, right, up and down
- **daylight_video** - filmed in a **daylight**, on which a person moves his/her head left, right, up and down
- **lightroom_video** - filmed in a **light room**, on which a person moves his/her head left, right, up and down
- **nightlight_video** - filmed in a **night light**, on which a person moves his/her head left, right, up and down
- **outline** -video of the person wearing a **printed 2D mask**
- **mask** - video of the person wearing a **printed 2D mask with cut-out holes for eyes**
- **monitor_video** - video of a person played on a computer and filmed on the phone

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The dataset comprises videos of genuine facial presentations using various methods, including printed 2D photos, masks as well as real and spoof faces. It proposes a novel approach that learns and extracts facial features to prevent spoofing attacks, based on deep neural networks and advanced biometric techniques.

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,
- **dataset_info.csvl** - includes the information about videos in the dataset

### File with the extension .csv
- **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*

原始信息汇总

数据集概述

数据集名称

• Biometric Attack Dataset - Different Lighting Conditions Dataset

数据集描述

• 该数据集包含个人和攻击视频，视频中显示了在不同光照条件（暗室、日光、亮室和夜灯）和不同地点（室内、室外）下的照片。每个视频的时长约为20秒。

视频类型

• darkroom_photo - 暗室中的个人照片显示在电脑上并由手机拍摄
• daylight_photo - 日光下的个人照片显示在电脑上并由手机拍摄
• lightroom_photo - 亮室中的个人照片显示在电脑上并由手机拍摄
• nightlight_photo - 夜灯下的个人照片显示在电脑上并由手机拍摄
• darkroom_video - 暗室中的个人头部左右上下移动的视频
• daylight_video - 日光下的个人头部左右上下移动的视频
• lightroom_video - 亮室中的个人头部左右上下移动的视频
• nightlight_video - 夜灯下的个人头部左右上下移动的视频
• outline - 个人佩戴打印的2D面具的视频
• mask - 个人佩戴带有眼睛切口的打印2D面具的视频
• monitor_video - 电脑上播放的个人视频并由手机拍摄

数据集内容

• files - 包含原始视频和攻击视频
• dataset_info.csv - 包含数据集中视频的信息

数据集文件信息

• file: 视频链接
• type: 视频类型

许可证

• cc-by-nc-nd-4.0

任务类别

• video-classification

语言

• en

标签

• code
• legal
• finance

商业使用

• 完整版本包含7296个视频，可通过**TrainingData**购买

数据集目的

• 该数据集用于通过深度神经网络和先进的生物识别技术学习和提取面部特征，以防止欺骗攻击。研究结果显示，该技术能有效防止未经授权的访问，并能识别未见的欺骗线索。