Published July 14, 2015 | Version v1
Dataset Restricted

Multispectral-Spoof (MSSpoof)

  • 1. Idiap Research Institute

Description

Multispectral-Spoof contains face images and printed spoofing attacks recorded in Visible (VIS) and Near-Infrared (NIR) spectra for 21 identities.

Multispectral-Spoof is a dataset for multi-spectral face recognition and presentation attack detection (anti-spoofing). The dataset contains images of Bona Fide VIS and NIR images as well as VIS and NIR printed presentation attacks (spoofing attacks) to VIS and NIR systems. The number of clients in the dataset is 21. The recordings are done using a uEye camera with resolution 1280x1024. When recording the images in NIR, a NIR filter of 800nm has been mounted to the camera.

 

Recording the real accesses

The set of real accesses contains recordings of VIS and NIR images for the identities. For each identity, a total of 5 images in VIS and 5 images in NIR are recorded for each of 7 environment conditions. 1 of these conditions is set up in an hallway space, while the rest 6 conditions are set up in an office. So, in total, the database contains 7*5=35 images per client in VIS and 7*5=35 images per client in NIR spectrum. Thus, the total number of real accesses per client is 70.

 

Recording the spoofing attacks

For each client in the database, 3 images in VIS and 3 images in NIR are selected from the original database. The chosen images are from the ones with the best quality in terms of recording conditions. These images are then printed on a paper using black & white printed with resolution of 600dpi. During the recording of the spoofing attacks, the printed images are attached on a fixed support. For each of the printed images, we recorded 4 spoofing attacks in 3 lighting conditions, both in VIS and NIR spectra. Thus, the total number of spoofing attacks per client is 6 * 2 * 3 * 4 = 144.

 

Database protocol

The recorded images are divided into train, development and test set, and the clients in each of the sets do not overlap. There are 9 clients in the train subset, 6 in the development and 6 in the test subset. The subsets do not overlap, meaning that a client in one subset can not appear in any other subset.

  • Client IDs in the world subset: 2,4,7,9,10,12,16,17,21
  • Client IDs in the dev subset: 1,3,5,6,8,11
  • Client IDs in the test subset: 13,15,18,19,20,22

Out of the 75 real access images per client, 10 are taken into the enrollment set: 5 from VIS and 5 from NIR spectra.

 

Reference paper

I. Chingovska, N. Erdogmus, A. Anjos. S. Marcel, “Face Recognition Systems Under Spoofing Attacks”, in Springer “Face Recognition Across the Imaging Spectrum” (Editor Thirimachos Bourlai), 2016.
10.1007/978-3-319-28501-6_8
https://publications.idiap.ch/index.php/publications/show/3539

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Additional details

Related works

Is documented by
Book chapter: 10.1007/978-3-319-28501-6_8 (Handle)

Funding

European Commission
TABULA RASA – Trusted Biometrics under Spoofing Attacks 257289