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

By Zinelabinde Boulkenafet, Jukka Komulainen, Xiaoyi Feng, Abdenour Hadid. IEEE International Conference on Automatic Face & Gesture Recognition, 2017

580 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/57e736120e3c5d3dce58616ea8433586c43eaeea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xiaogang Wang
2. Luc Van Gool
3. Xiaoming Liu
4. Dacheng Tao
5. Xilin Chen
6. Chen Change Loy
7. Yi Yang
8. Yu Qiao

In Information Systems

1. Xiaoming Liu
2. Yaojie Liu
3. Amin Jourabloo
4. Shiguang Shan
5. Xilin Chen
6. Guoying Zhao
7. Zitong Yu
8. Xiaobai Li

In Signal Processing

1. Xiaoming Liu
2. Yaojie Liu
3. Amin Jourabloo
4. Xilin Chen
5. Shiguang Shan
6. Zitong Yu
7. Guoying Zhao
8. Hu Han

Citations in the corpus

• Deep Tree Learning for Zero-Shot Face Anti-Spoofing (2019, methodology): “2 compares the proposed method with top three methods selected from over 20 methods in [3, 9, 45].”
• Searching Central Difference Convolutional Networks for Face Anti-Spoofing (2020, methodology): “Six databases OULU-NPU [10], SiW [36], CASIA-MFSD [66], Replay-Attack [13], MSU-MFSD [57] and SiW-M [37] are used in our experiments.”
• Face Anti-Spoofing: Model Matters, so Does Data (2019, methodology): “We strictly follow the four testing protocols on Oulu-NPU and the three protocols on SiW for the evaluation.”
• Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing (2020, methodology): “The predicted depth maps of hard samples in OULU-NPU Protocol 3 are partly visualized in Fig.”
• Domain Generalization: A Survey (2021, unknown): “A common practice is to combine several face anti-spoofing datasets for model training and do evaluation on an unseen dataset, e.g., using CASIA-MFSD [79], Oulu-NPU [80] and MSU-MFSD [81] as the sources and Idiap Replay-Attack [82] as the target.”
• Single-Side Domain Generalization for Face Anti-Spoofing (2020, methodology): “Four public face anti-spoofing databases are utilized to evaluate the effectiveness of our method: OULU-NPU [7] (denoted as O), CASIA-FASD [45] (denoted as C), Idiap Replay-Attack [8] (denoted as I), and MSU-MFSD [39] (denoted as M).”
• CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing (2019, methodology): “Oulu-NPU dataset: It is a high-resolution dataset, consisting of 4 , 950 real access and spoofing videos with many real-world variations.”
• Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection (2019, background): “Sample frames from CASIA-MFSD [35], Idiap Replay-Attack [8], MSU-MFSD [31], and Oulu-NPU [4] datasets.”
• Deep Anomaly Detection for Generalized Face Anti-Spoofing (2019, background): “Replay-Mobile [10], OULU-NPU [6]), or a webcam setting, where the user is placed in front of a fixed camera ( e.g .”
• Face De-Spoofing: Anti-Spoofing via Noise Modeling (2018, methodology): “Oulu-NPU [43] is a high-resolution database, considering many real-world variations.”
• Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning (2020, methodology): “We provide evaluations on four widely used databases for cross-domain PAD including Idiap REPLAY-ATTACK [9] (Idiap), CASIA Face AntiSpoofing [52] (CASIA), MSU-MFSD [48] (MSU) and OULU-NPU [7] (OULU).”
• Face Anti-Spoofing with Human Material Perception (2020, methodology): “est) + mean(P test): (8) 4.3 Ablation Study In this subsection, all ablation studies are conducted on Protocol-1 (dierent illumination condition and location between train and test sets) of OULU-NPU [58] to explore the details of our proposed BCN, MFRM and multi-head supervision. Impact of ˙ r and B”
• CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations (2020, background): “APCER, BPCER, ACER, EER, and HTER) [4,23,15,36] and add another two criteria (i.”