OULU-NPU: A Mobile Face Presentation Attack Database with Real-World Variations

Abstract

The vulnerabilities of face-based biometric systems to presentation attacks have been finally recognized but yet we lack generalized software-based face presentation attack detection (PAD) methods performing robustly in practical mobile authentication scenarios. This is mainly due to the fact that the existing public face PAD datasets are beginning to cover a variety of attack scenarios and acquisition conditions but their standard evaluation protocols do not encourage researchers to assess the generalization capabilities of their methods across these variations. In this present work, we introduce a new public face PAD database, OULU-NPU, aiming at evaluating the generalization of PAD methods in more realistic mobile authentication scenarios across three covariates: unknown environmental conditions (namely illumination and background scene), acquisition devices and presentation attack instruments (PAI). This publicly available database consists of 5940 videos corresponding to 55 subjects recorded in three different environments using high-resolution frontal cameras of six different smartphones. The high-quality print and videoreplay attacks were created using two different printers and two different display devices. Each of the four unambiguously defined evaluation protocols introduces at least one previously unseen condition to the test set, which enables a fair comparison on the generalization capabilities between new and existing approaches. The baseline results using color texture analysis based face PAD method demonstrate the challenging nature of the database.

CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing

“…Even the recently released SiW [15] dataset, collected with high resolution image quality, only contains RGB data. With the widespread application of face recognition in mobile phones, there are also some RGB datasets recorded by replaying face video with smartphone, such as MSU-MFSD [11], Replay-Mobile [12] and OULU-NPU [14].…”

“…In this subsection, we evaluate the generalization capability of the proposed dataset on the Oulu-NPU [14], SiW [15] and CASIA-MFSD [9] datasets. The CASIA-SURF dataset contains not only RGB images, but also the corresponding Depth information, which is indeed beneficial for Depth supervised face anti-spoofing methods [15], [53].…”

“…In recent years, face PAD algorithms [15], [16] have achieved great performances. One of the key points of this success is the availability of face anti-spoofing benchmark datasets [8], [9], [11], [12], [14], [15]. However, there are several shortcomings in the existing datasets as follows:…”

“…How to compute the performance of algorithms is an open issue in face anti-spoofing. Many works [12], [14]- [16] adopt the Attack Presentation Classification Error Rate (APCER), the Normal Presentation Classification Error Rate (NPCER) and the Average Classification Error Rate (ACER) as the evaluation metric, in which APCER and NPCER are used to measure the error rate of fake or arXiv:1908.10654v1 [cs.CV] 28 Aug 2019 live samples, and ACER is the average of APCER and NPCER scores. However, in real applications, one may be more concerned about the false positive rate, i.e., attacker is treated as real/live one.…”

The Swax Benchmark: Attacking Biometric Systems with Wax Figures

“…Such alignment algorithms tend to improve biometric systems performance due to carefully positioning subject faces into a canonical pose. For Protocol 04, DE-SPOOFING [17] performs a cross-dataset evaluation as it is trained on OULU-NPU dataset [7] and then aims to estimate deep spoof noises from SWAX probe face samples.…”