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

Tasks:

Video Classification

Languages: English

Tags: code legal finance

License: cc-by-nc-nd-4.0

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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"

Biometric Attacks in Different Lighting Conditions Dataset

The 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.

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
mask - video of the person wearing a printed 2D mask
outline - 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 serves as a valuable resource for computer vision, anti-spoofing tasks, video analysis, and security systems. It allows for the development of algorithms and models that can effectively detect attacks.

Studying the dataset may lead to the development of improved security systems, surveillance technologies, and solutions to mitigate the risks associated with masked individuals carrying out attacks.