Face Anti-Spoofing from the Perspective of Data Sampling

• URL: http://arxiv.org/abs/2208.13164v1
• Date: Sun, 28 Aug 2022 07:54:30 GMT
• Title: Face Anti-Spoofing from the Perspective of Data Sampling
• Authors: Usman Muhammad and Mourad Oussalah
• Abstract summary: Face presentation attack detection plays a vital role in providing secure facial access to digital devices. Most existing video-based PAD countermeasures lack the ability to cope with long-range temporal variations in videos. This paper proposes a video processing scheme that models the long-range temporal variations based on Gaussian Weighting Function.
• Score: 0.342658286826597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Without deploying face anti-spoofing countermeasures, face recognition systems can be spoofed by presenting a printed photo, a video, or a silicon mask of a genuine user. Thus, face presentation attack detection (PAD) plays a vital role in providing secure facial access to digital devices. Most existing video-based PAD countermeasures lack the ability to cope with long-range temporal variations in videos. Moreover, the key-frame sampling prior to the feature extraction step has not been widely studied in the face anti-spoofing domain. To mitigate these issues, this paper provides a data sampling approach by proposing a video processing scheme that models the long-range temporal variations based on Gaussian Weighting Function. Specifically, the proposed scheme encodes the consecutive t frames of video sequences into a single RGB image based on a Gaussian-weighted summation of the t frames. Using simply the data sampling scheme alone, we demonstrate that state-of-the-art performance can be achieved without any bells and whistles in both intra-database and inter-database testing scenarios for the three public benchmark datasets; namely, Replay-Attack, MSU-MFSD, and CASIA-FASD. In particular, the proposed scheme provides a much lower error (from 15.2% to 6.7% on CASIA-FASD and 5.9% to 4.9% on Replay-Attack) compared to baselines in cross-database scenarios.

Related papers

• Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing [5.543184872682789]
  Face presentation attacks (PA), also known as spoofing attacks, pose a substantial threat to biometric systems. Several video-based methods have been presented in the literature that analyze facial motion in successive video frames. In this paper, we rephrase the face anti-spoofing task as a motion prediction problem and introduce a deep ensemble learning model with a frame skipping mechanism.
  arXiv  Detail & Related papers  (2023-07-06T08:50:29Z)
• Seq-Masks: Bridging the gap between appearance and gait modeling for video-based person re-identification [10.490428828061292]
  ideo-based person re-identification (Re-ID) aims to match person images in video sequences captured by disjoint surveillance cameras. Traditional video-based person Re-ID methods focus on exploring appearance information, thus, vulnerable against illumination changes, scene noises, camera parameters, and especially clothes/carrying variations. We propose a framework that utilizes the sequence masks (SeqMasks) in the video to integrate appearance information and gait modeling in a close fashion.
  arXiv  Detail & Related papers  (2021-12-10T16:00:20Z)
• Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning [54.15303628138665]
  Face anti-spoofing (FAS) plays a vital role in preventing face recognition systems from presentation attacks. Existing face anti-spoofing datasets lack diversity due to the insufficient identity and insignificant variance. We propose Dual Spoof Disentanglement Generation framework to tackle this challenge by "anti-spoofing via generation"
  arXiv  Detail & Related papers  (2021-12-01T15:36:59Z)
• Aurora Guard: Reliable Face Anti-Spoofing via Mobile Lighting System [103.5604680001633]
  Anti-spoofing against high-resolution rendering replay of paper photos or digital videos remains an open problem. We propose a simple yet effective face anti-spoofing system, termed Aurora Guard (AG)
  arXiv  Detail & Related papers  (2021-02-01T09:17:18Z)
• Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction [61.17654438176999]
  We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design. Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
  arXiv  Detail & Related papers  (2020-11-05T11:34:12Z)
• Sharp Multiple Instance Learning for DeepFake Video Detection [54.12548421282696]
  We introduce a new problem of partial face attack in DeepFake video, where only video-level labels are provided but not all the faces in the fake videos are manipulated. A sharp MIL (S-MIL) is proposed which builds direct mapping from instance embeddings to bag prediction. Experiments on FFPMS and widely used DFDC dataset verify that S-MIL is superior to other counterparts for partially attacked DeepFake video detection.
  arXiv  Detail & Related papers  (2020-08-11T08:52:17Z)
• Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing [61.82466976737915]
  Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing. We propose a new approach to detect presentation attacks from multiple frames based on two insights. The proposed approach achieves state-of-the-art results on five benchmark datasets.
  arXiv  Detail & Related papers  (2020-03-18T06:11:20Z)
• Face Anti-Spoofing by Learning Polarization Cues in a Real-World Scenario [50.36920272392624]
  Face anti-spoofing is the key to preventing security breaches in biometric recognition applications. Deep learning method using RGB and infrared images demands a large amount of training data for new attacks. We present a face anti-spoofing method in a real-world scenario by automatic learning the physical characteristics in polarization images of a real face.
  arXiv  Detail & Related papers  (2020-03-18T03:04:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.