Face Presentation Attack With Latex Masks in Multispectral Videos

Akshay Agarwal, Daksha Yadav, Naman Kohli, Richa Singh, Mayank Vatsa, Afzel Noore; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 81-89

Abstract


Face recognition systems are susceptible to presentation attacks such as printed photo attacks, replay attacks, and 3D mask attacks. These attacks, primarily studied in visible spectrum, aim to obfuscate or impersonate a person's identity. This paper presents a unique multispectral video face database for face presentation attack using latex and paper masks. The proposed Multispectral Latex Mask based Video Face Presentation Attack (MLFP) database contains 1350 videos in visible, near infrared, and thermal spectrums. Since the database consists of videos of subjects without any mask as well as wearing ten different masks, the effect of identity concealment is analyzed in each spectrum using face recognition algorithms. We also present the performance of existing presentation attack detection algorithms on the proposed MLFP database. It is observed that the thermal imaging spectrum is most effective in detecting face presentation attacks.

Related Material


[pdf]
[bibtex]
@InProceedings{Agarwal_2017_CVPR_Workshops,
author = {Agarwal, Akshay and Yadav, Daksha and Kohli, Naman and Singh, Richa and Vatsa, Mayank and Noore, Afzel},
title = {Face Presentation Attack With Latex Masks in Multispectral Videos},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}