Masked fake face detection using radiance measurements.

By Youngshin Kim, Jaekeun Na, Seongbeak Yoon, Juneho Yi. 2009

101 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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. S. Li
2. Sébastien Marcel
3. Zhen Lei
4. Xiaogang Wang
5. Luc Van Gool
6. Xiaoming Liu
7. Dacheng Tao
8. Junjie Yan

Citations in the corpus

• CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing (2019, background): “[22] introduce a new dataset to distinguish between the facial skin and mask materials by exploiting their reflectance.”
• Face liveness detection by learning multispectral reflectance distributions (2011, methodology): “Reasons for different detection accuracy between ours and [4] are: 1) the mask faces used in the experiments are different; 2) they fix the distance as 30cm, and use the forehead for test.”
• Biometric Antispoofing Methods: A Survey in Face Recognition (2014, background): “However, some interesting methods have been proposed base on imaging technology outside the visual spectrum, such as: complementary infrared (IR) or near infrared (NIR) images, which are even claimed to provide sufficient information to distinguish between identical twins [129], [152]; comparing the”
• Spoofing Face Recognition With 3D Masks (2014, background): “In [19], the authors conduct experiments on different mask materials such as silicon, latex or skin-jell to see how different they behave in reflectance when compared to facial skin that is sampled from the forehead region.”
• A face antispoofing database with diverse attacks (2012, background): “It is important to mention that multi-spectral [7, 8, 9] and multi-modality [10, 11] based anti-spoofing methods also perform quite well by exploring other liveness clues such as face images under nonvisible illuminations or voice information.”
• Spoofing in 2D face recognition with 3D masks and anti-spoofing with Kinect (2013, background): “[9] can be listed as one of the first papers published in mask anti-spoofing.”