Motion-based counter-measures to photo attacks in face recognition

By André Anjos, Murali Mohan Chakka, Sébastien Marcel. 2014

166 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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. Dacheng Tao
2. Sébastien Marcel
3. Xilin Chen
4. Luc Van Gool
5. Shiguang Shan
6. Xiaogang Wang
7. Zhen Lei
8. S. Li

In Signal Processing

1. Sébastien Marcel
2. Abdenour Hadid
3. Ramachandra Raghavendra
4. Christoph Busch
5. Xilin Chen
6. Shiguang Shan
7. Julian Fierrez
8. Jukka Komulainen

Citations in the corpus

• Robust multimodal face and fingerprint fusion in the presence of spoofing attacks (2016, background): “A good overview on face counter-spoofing may be found in [23].”
• Presentation Attack Detection Methods for Face Recognition Systems (2017, background): “The captured motion is particularly due to the movements of the head [De Marsico et al. 2012; Anjos et al. 2014], mouth [Kollreider et al.”
• Face Biometrics Under Spoofing Attacks: Vulnerabilities, Countermeasures, Open Issues, and Research Directions (2014, methodology): “An extension of this database, called the PHOTO-ATTACK database, providing photo attacks using different attack media such as mobile phones and tablets was introduced in [1].”
• Single-Side Domain Generalization for Face Anti-Spoofing (2020, methodology): “In contrast, the temporal-based methods leverage various temporal cues in consecutive face frames, such as rPPG [20, 21] and optical flow [2, 4].”
• Biometric Antispoofing Methods: A Survey in Face Recognition (2014, background): “, nodding, smiling, looking in different directions) detected trough face and gaze tracking [134], [135] or through optical flow estimation [27], [103], [136], [137].”
• Face Anti-spoofing: Visual Approach (2014, methodology): “As demonstrated inwork byAnjos and others [16, 17], techniques for antispoofing can also exploit motion artifacts present in attacks to discriminate spoofing attempts.”
• Learn Convolutional Neural Network for Face Anti-Spoofing (2014, background): “proposed to recognize spoofing attacks based on the correlation between optical flows in foreground and background regions [1].”