Open AccessPixel‐wise supervision for presentation attack detection on identity document cards
Author(s) -
Mudgalgundurao Raghavendra,
Schuch Patrick,
Raja Kiran,
Ramachandra Raghavendra,
Damer Naser
Publication year - 2022
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/bme2.12088
Subject(s) - computer science , liveness , presentation (obstetrics) , biometrics , leverage (statistics) , pixel , artificial intelligence , identity (music) , word error rate , identification (biology) , replay attack , computer security , computer vision , speech recognition , authentication (law) , medicine , physics , botany , biology , acoustics , radiology , programming language
Identity documents (or IDs) play an important role in verifying the identity of a person with wide applications in banks, travel, video‐identification services and border controls. Replay or photocopied ID cards can be misused to pass ID control in unsupervised scenarios if the liveness of a person is not checked. To detect such presentation attacks on ID card verification process when presented virtually is a critical step for the biometric systems to assure authenticity. In this paper, a pixel‐wise supervision on DenseNet is proposed to detect presentation attacks of the printed and digitally replayed attacks. The authors motivate the approach to use pixel‐wise supervision to leverage minute cues on various artefacts such as moiré patterns and artefacts left by the printers. The baseline benchmark is presented using different handcrafted and deep learning models on a newly constructed in‐house database obtained from an operational system consisting of 886 users with 433 bona fide, 67 print and 366 display attacks. It is demonstrated that the proposed approach achieves better performance compared to handcrafted features and Deep Models with an Equal Error Rate of 2.22% and Bona fide Presentation Classification Error Rate (BPCER) of 1.83% and 1.67% at Attack Presentation Classification Error Rate of 5% and 10%.