File size: 3,795 Bytes
fee5675
 
fc1a722
 
 
 
 
 
 
 
fee5675
fc1a722
dbd23c7
 
fc1a722
dbd23c7
2c515b0
dbd23c7
fc1a722
 
 
dbd23c7
 
 
 
fc1a722
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
dbd23c7
 
 
83f85c0
 
 
dbd23c7
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
---
license: cc-by-nc-nd-4.0
task_categories:
- video-classification
language:
- en
tags:
- finance
- legal
- code
---

# Low Quality Live Attacks - Biometric Attack dataset
The anti spoofing dataset includes live-recorded Anti-Spoofing videos from around the world, captured via **low-quality** webcams with resolutions like QVGA, QQVGA and QCIF. The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users. 

The dataset contains images and videos of real humans with various **views, and colors**, making it a comprehensive resource for researchers working on anti-spoofing technologies.

# 💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on **[TrainingData](https://trainingdata.pro/datasets/low-quality-webcam-attacks?utm_source=huggingface&utm_medium=cpc&utm_campaign=low_quality_webcam_video_attacks)** to buy the dataset

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F43bc66b1f16995fb42f10075db8f9ba5%2F4.png?generation=1684704084546644&alt=media)

The dataset provides data to combine and apply different techniques, approaches, and models to address the challenging task of distinguishing between genuine and spoofed inputs, providing effective anti-spoofing solutions in active authentication systems. These solutions are crucial as newer devices, such as phones, have become vulnerable to spoofing attacks due to the availability of technologies that can create replays, reflections, and depths, making them susceptible to spoofing and generalization. 

Our dataset also explores the use of neural architectures, such as deep neural networks, to facilitate the identification of distinguishing patterns and textures in different regions of the face, increasing the accuracy and generalizability of the anti-spoofing models. 

# Webcam Resolution
The collection of different video resolutions is provided, like:
- QVGA (320p x 240p), 
- QQVGA (120p x 160p),
- QCIF (176p x 144p) and others.

# Metadata

Each attack instance is accompanied by the following details:

- Unique attack identifier
- Identifier of the user recording the attack
- User's age
- User's gender
- User's country of origin
- Attack resolution

Additionally, the model of the webcam is also specified.

Metadata is represented in the `file_info.csv`.

# 💴 Buy the Dataset: This is just an example of the data. Leave a request on **[https://trainingdata.pro](https://trainingdata.pro/datasets/low-quality-webcam-attacks?utm_source=huggingface&utm_medium=cpc&utm_campaign=low_quality_webcam_video_attacks)** to discuss your requirements, learn about the price and buy the dataset.

## **[TrainingData](https://trainingdata.pro/datasets/low-quality-webcam-attacks?utm_source=huggingface&utm_medium=cpc&utm_campaign=low_quality_webcam_video_attacks)** 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: 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, ibeta dataset, human video dataset, video dataset, low quality video dataset, phone attack dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset*