Multi-Modal Face Anti-Spoofing Attack Detection Challenge at CVPR2019

Liu, Ajian; Wan, Jun; Escalera, Sérgio; Escalante, Hugo Jair; Tan, Zichang; Yuan, Qi; Wang, Kai; Lin, Chao; Guo, Guodong; Guyon, Isabelle; Li, Stan Z.

doi:10.1109/cvprw.2019.00202

Cited by 69 publications

(48 citation statements)

References 25 publications

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“…orblueMost recent works in general face PAD (e.g. in the context of the ChaLearn PAD challenge [34]) utilised depth and/or near-infrared data coupled with CNNs. While they achieved promising results, the requirement of additional sensors to acquire the multi-modal information may be prohibitive for operational systems, which overwhelmingly rely on 2D RGB images only.…”

Section: Related Workmentioning

confidence: 99%

Makeup Presentation Attacks: Review and Detection Performance Benchmark

2020

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Section: Related Workmentioning

confidence: 99%

Makeup Presentation Attacks: Review and Detection Performance Benchmark

2020

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“…More recently, after the release of the large-scale multi-modal face antispoofing dataset, CASIA-SURF [113], the Chalearn LAP Multi-Modal Face antispoofing Attack Detection Challenge [114] was held in 2019. Thirteen teams were qualified for the final round with all submitted face PAD solutions relying on CNN-based feature extractors.…”

Section: Competitionsmentioning

confidence: 99%

Deep convolutional neural networks for face and iris presentation attack detection: survey and case study

2020

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Biometric presentation attack detection (PAD) is gaining increasing attention. Users of mobile devices find it more convenient to unlock their smart applications with finger, face, or iris recognition instead of passwords. In this study, the authors survey the approaches presented in the recent literature to detect face and iris presentation attacks. Specifically, they investigate the effectiveness of fine-tuning very deep convolutional neural networks to the task of face and iris antispoofing. They compare two different fine-tuning approaches on six publicly available benchmark datasets. Results show the effectiveness of these deep models in learning discriminative features that can tell apart real from fake biometric images with a very low error rate. Cross-dataset evaluation on face PAD showed better generalisation than state-of-the-art. They also performed cross-dataset testing on iris PAD datasets in terms of equal error rate, which was not reported in the literature before. Additionally, they propose the use of a single deep network trained to detect both face and iris attacks. They have not noticed accuracy degradation compared to networks trained for only one biometric separately. Finally, they analysed the learned features by the network, in correlation with the image frequency components, to justify its prediction decision.

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“…The extraction and fusion of different features in multimodal scenario [ 4 ] has gradually been popularized in anti-spoofing paradigm. Liu et al [ 5 ] presented the challenges in replay attack detection based on a multimodal face anti-spoofing dataset CASIA-SURF. However, in facial biometric domain anti spoofing approaches are limited to some extent.…”

Section: Introductionmentioning

confidence: 99%

Spoofed Facial Presentation Attack Detection by Multivariate Gradient Descriptor in Micro-Expression Region

Karmakar¹,

Mukherjee²,

Datta

2021

Pattern Recognit. Image Anal.

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Facial video presentation is a topic of interest in many security systems due to its non-intrusive nature. However, such systems are vulnerable to spoof attacks made by fake face videos and thereby gaining unauthorized access in the system. For a robust biometric system anti spoofing approaches like liveness detection ought to be implemented in order to counter the aforesaid print and replay attacks. This article proposes a novel approach of anti spoofing using Multivariate histogram of oriented gradients descriptor in the auto detected micro expression (μE) regions of human facial videos. Facial μE are very brief, spontaneous facial expressions that highlight the face of humans when they either unconsciously or deliberately conceal an emotion. The work emphasizes the variations in μE in fake and original video representation by a considerable amount and claims such a variance is a tool to combat against presentation attacks. In particular, the method automatically extracts the ROI of major changes in μE using the Multivariate orientation gradients parameter and thus proposes this descriptor as one of the most suitable tools to characterize the liveness. The entire implementation is carried out on the self-created Database for replay attacks. The result obtained is satisfactory and tested statistically significant.

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Multi-Modal Face Anti-Spoofing Attack Detection Challenge at CVPR2019

Cited by 69 publications

References 25 publications

Makeup Presentation Attacks: Review and Detection Performance Benchmark

Makeup Presentation Attacks: Review and Detection Performance Benchmark

Deep convolutional neural networks for face and iris presentation attack detection: survey and case study

Spoofed Facial Presentation Attack Detection by Multivariate Gradient Descriptor in Micro-Expression Region

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