Efficient Face Spoofing Detection With Flash

In light of the rising demand for biometric-authentication systems, preventing face spoofing attacks is a critical issue for the safe deployment of face recognition systems. Here, we propose an efficient face presentation attack detection (PAD) algorithm that requires minimal hardware and only a sma...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on biometrics, behavior, and identity science behavior, and identity science, 2021-10, Vol.3 (4), p.535-549
Main Authors: Ebihara, Akinori F., Sakurai, Kazuyuki, Imaoka, Hitoshi
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Artificial neural networks
Biometrics (access control)
Classifiers
diffuse reflection
edge device
efficient learning
Face recognition
Face spoofing detection
Faces
small data
Specular reflection
Spoofing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In light of the rising demand for biometric-authentication systems, preventing face spoofing attacks is a critical issue for the safe deployment of face recognition systems. Here, we propose an efficient face presentation attack detection (PAD) algorithm that requires minimal hardware and only a small database, making it suitable for resource-constrained devices such as mobile phones. Utilizing one monocular visible-light camera, the proposed algorithm takes two facial photos, taken with and without a flash, respectively. The proposed SpecDiff descriptor is constructed by leveraging two types of reflection: (i) specular reflections from the iris region that have a specific intensity distribution depending on liveness, and (ii) diffuse reflections from the entire face region that represents the 3D structure of a subject's face. Classifiers trained with the SpecDiff descriptor outperform other flash-based PAD algorithms on both an in-house database and four publicly available databases: NUAA, Replay-Attack, Spoofing in the Wild, and OULU-NPU. Furthermore, the proposed algorithm achieves statistically significantly better accuracy to that of an end-to-end, deep neural network classifier, while being approximately six-times faster execution speed. The limitation of the proposed algorithm is also quantified under various adversarial lighting conditions, to guide users for the safe deployment of the algorithm. The code is publicly available at https://github.com/Akinori-F-Ebihara/SpecDiff-spoofing-detector . Example images of in-house database are also available at https://github.com/Akinori-F-Ebihara/SpecDiff_in_house_database_sample .
ISSN:2637-6407
2637-6407
DOI:10.1109/TBIOM.2021.3076816