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 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

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 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

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file string	type string
0.mp4	darkroom_photo
1.mp4	lightroom_photo
2.mp4	lightroom_photo
3.mp4	darkroom_video
4.mp4	monitor_video
5.mp4	darkroom_video
6.mp4	daylight_video
7.mp4	monitor_video
8.mp4	monitor_video
9.mp4	daylight_photo
10.mp4	lightroom_photo
11.mp4	daylight_photo
12.mp4	daylight_video
13.mp4	daylight_video
14.mp4	monitor_video
15.mp4	monitor_video
16.mp4	monitor_video
17.mp4	daylight_video
18.mp4	lightroom_photo
19.mp4	lightroom_photo
20.mp4	lightroom_photo
21.mp4	monitor_video
22.mp4	monitor_video
23.mp4	monitor_video
24.mp4	lightroom_video
25.mp4	lightroom_video
26.mp4	mask
27.mp4	lightroom_video
28.mp4	mask
29.mp4	lightroom_photo
30.mp4	lightroom_photo
31.mp4	lightroom_photo
32.mp4	monitor_video
33.mp4	monitor_video
34.mp4	monitor_video
35.mp4	nightlight_photo
36.mp4	lightroom_photo
37.mp4	monitor_video
38.mp4	monitor_video
39.mp4	nightlight_video
40.mp4	monitor_video
41.mp4	daylight_video
42.mp4	daylight_video
43.mp4	outline
44.mp4	outline
45.mp4	outline
46.mp4	lightroom_video
47.mp4	lightroom_video
48.mp4	lightroom_video
49.mp4	daylight_video
50.mp4	daylight_video
51.mp4	lightroom_video
52.mp4	lightroom_video
53.mp4	daylight_video