3D face mask presentation attack detection based on intrinsic image analysis

Li, Lei; Xia, Zhaoqiang; Jiang, Xiaoyue; Ma, Yupeng; Roli, Fabio; Feng, Xiaoyi

doi:10.1049/iet-bmt.2019.0155

Cited by 24 publications

(8 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, head motions and rPPG signals are easily imitated in the replay attack, making such dynamic clues less reliable. Basing on the fact that replay attacks usually have abnormal reflection changes, Li et al [116] propose to capture such illumination changes using a 1D CNN with inputs of the intensity difference histograms from reflectance images.…”

Section: Hybrid (Handcraft + Deep Learning) Methodsmentioning

confidence: 99%

Deep Learning for Face Anti-Spoofing: A Survey

Qin

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, early-stage FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.

show abstract

Section: Hybrid (Handcraft + Deep Learning) Methodsmentioning

confidence: 99%

Deep Learning for Face Anti-Spoofing: A Survey

Qin

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

show abstract

“…In terms of vision applications, concepts of human material perception have been developed into image quality assessment [45] and video quality assessment [46]. For face anti-spoofing task, few works [17,18,47] consider discrepant surface reflectance properties of live or spoofing faces. However, only considering surface reflectance properties is not always reliable for material perception [1].…”

Section: Human and Machine Materials Perceptionmentioning

confidence: 99%

Face Anti-Spoofing with Human Material Perception

Niu

et al. 2020

Preprint

View full text Add to dashboard Cite

Face anti-spoofing (FAS) plays a vital role in securing the face recognition systems from presentation attacks. Most existing FAS methods capture various cues (e.g., texture, depth and reflection) to distinguish the live faces from the spoofing faces. All these cues are based on the discrepancy among physical materials (e.g., skin, glass, paper and silicone). In this paper we rephrase face anti-spoofing as a material recognition problem and combine it with classical human material perception [1], intending to extract discriminative and robust features for FAS. To this end, we propose the Bilateral Convolutional Networks (BCN), which is able to capture intrinsic material-based patterns via aggregating multi-level bilateral macro-and micro-information. Furthermore, Multilevel Feature Refinement Module (MFRM) and multi-head supervision are utilized to learn more robust features. Comprehensive experiments are performed on six benchmark datasets, and the proposed method achieves superior performance on both intra-and cross-dataset testings. One highlight is that we achieve overall 11.3±9.5% EER for cross-type testing in SiW-M dataset, which significantly outperforms previous results. We hope this work will facilitate future cooperation between FAS and material communities.

show abstract

“…The proposed solution derives texture-dependent functionality from isolated wavelet processed images dependent on Local Binary Se-quence against Dataset 3D mask attack. Li et al [11] suggested an innovative 3D face mask attack detection approach based on visual refractive analysis. The face picture was first analyzed in the proposed approach using an inherent picture decomposition technique to measure the image reflectance.…”

Section: Related Workmentioning

confidence: 99%

Face Mask Detection Classifier and Model Pruning with Keras-Surgeon

Negi

Chauhan

Kumar

et al. 2020

2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)

View full text Add to dashboard Cite

Multidisciplinary initiatives in the new world of coronavirus were combined to limit the spread of the pandemic. Interestingly, the AI group was a part of those efforts. This result-based approach is used to help scan, assess, predict and track current patients and possibly potential patients. Developments for tracking social distances or recognizing face masks have made headlines in particular. Most current advanced approaches to face mask recognition are built based on deep learning which is dependent on a large number of face samples. Nearly everybody wears a mask during corona virus outbreak in order to effectively avoid the spread of COVID-19 virus. Our goal is to train a customized deep learning model that helps to detect even if or not a person wears a mask and study the concept of model pruning with Keras-Surgeon. Model pruning can be efficient in reducing model size, so that it can be easily implemented and inferred on embedded systems.

show abstract

3D face mask presentation attack detection based on intrinsic image analysis

Cited by 24 publications

References 42 publications

Deep Learning for Face Anti-Spoofing: A Survey

Deep Learning for Face Anti-Spoofing: A Survey

Face Anti-Spoofing with Human Material Perception

Face Mask Detection Classifier and Model Pruning with Keras-Surgeon

Contact Info

Product

Resources

About