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

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

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.

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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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