MLFP

Multispectral Latex Mask based Video Face Presentation Attack (MLFP)

The MLFP database [1] contains the real and mask attack videos in visible, near-infrared, thermal spectrum. The database contains 1,350 videos in total, out of which 1,200 videos are attack videos and 150 videos are real access videos. The database consists of 10 subjects (4 females and 6 males) between the age of 23-38 years.

The real access videos are captured in different sessions at two different locations: Indoor and Outdoor of office. The videos are captured in all three spectrums: Visible, Near-Infrared, and Thermal. For each subject 4 real access videos are captured which represents two locations and two sessions. In total, database contains 40 videos of 10 subjects in each spectrum. Other than this one more real access video is captured for each subject for face identification experiments, which is not used for presentation attack detection.

Collection of Mask Attack Videos:

In the MLFP database, two types of face masks are utilized:

3D Latex Masks: Seven different latex masks are used for attack videos collection. These masks allow life-like movement of mouth as well as face. While six masks cover the entire face, the seventh mask is a half-mask which covers the face region below the eyes.
2D Paper Masks: Three paper masks are utilized with cutouts for eyes. These are created using high resolution images on high quality card paper.

The directory structure for VIS spectrum:

VIS--> Session (1 and 2) --> Environment folder (Indoor and Outdoor) --> Subject folders ( 1 to 10) --> Type of Videos (10 mask attack and 1 real). Folder name M represents the mask attack videos and R represents the real video. For each subject, there are 4 real and 40 mask attack videos per spectrum in the database. Each spectrum follows the same directory structure.

Database Protocol:

The MLFP database contains videos from 10 subjects wearing 10 different masks. The database is divided both based on subject and masks unseen training-testing protocol. For example, for one fold 3 random subjects are selected where each subjects 10 mask videos are there, out of these 9 (because the tenth mask which is unique as it is a half mask, is always utilized in the testing set) you randomly chose one paper mask and two latex masks in the training fold of these 3 subjects while the remaining unseen masks are utilized for testing from the remaining subjects (i.e., 7).

Training				Testing
Subject Folds	Ids	Mask Folds	Ids	Subject Folds	Ids	Mask Folds	Ids
1	1, 2, and 3	1	2, 3, and 8	1	4 to 10	1	1,4,5,6,7,9,10
	1, 2, and 3	2	4,5, and 9		4 to 10	2	1,2,3,6,7,8,10
	1, 2, and 3	3	6, 7, and 10		4 to 10	3	1,2,3,4,5,8,9
2	4,5, and 6	1	2, 3, and 8	2	1 to 3 and 7 to 10	1	1,4,5,6,7,9,10
	4,5, and 6	2	4,5, and 9		1 to 3 and 7 to 10	2	1,2,3,6,7,8,10
	4,5, and 6	3	6, 7, and 10		1 to 3 and 7 to 10	3	1,2,3,4,5,8,9
3	7, 8, 9 and 10	1	2, 3, and 8	3	1 to 6	1	1,4,5,6,7,9,10
	7, 8, 9 and 10	2	4,5, and 9		1 to 6	2	1,2,3,6,7,8,10
	7, 8, 9 and 10	3	6, 7, and 10		1 to 6	3	1,2,3,4,5,8,9

The database can be downloaded from the following link.

Multispectral Latex Mask based Video Face Presentation Attack (MLFP) Database (46.89GB) (MD5: BF08F103164EB1E901A6C2AC8FC5F2E2, CRC: 0CA4190B)

To obtain the password for the compressed file, email the duly filled license agreement to databases@iab-rubric.org with the subject line "License agreement for Multispectral Latex Mask based Video Face Presentation Attack (MLFP)"
NOTE: The license agreement has to be signed by someone having the legal authority to sign on behalf of the institute, such as the head of the institution or registrar. If a license agreement is signed by someone else, it will not be processed further.

This database is available only for research and educational purpose and not for any commercial use. If you use the database in any publications or reports, you must refer to the following paper:

[1] A. Agarwal, D. Yadav, N. Kohli, R. Singh, M. Vatsa, and A. Noore. Face presentation attack with latex masks in multispectral videos. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 275–283, 2017.