Proceedings ArticleDOI
Face anti-spoofing with multifeature videolet aggregation
Talha Ahmad Siddiqui,Samarth Bharadwaj,Tejas I. Dhamecha,Akshay Agarwal,Mayank Vatsa,Richa Singh,Nalini K. Ratha +6 more
- pp 1035-1040
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TLDR
A novel multi-feature evidence aggregation method for face spoofing detection that fuses evidence from features encoding of both texture and motion properties in the face and also the surrounding scene regions and provides robustness to different attacks.Abstract:
Biometric systems can be attacked in several ways and the most common being spoofing the input sensor. Therefore, anti-spoofing is one of the most essential prerequisite against attacks on biometric systems. For face recognition it is even more vulnerable as the image capture is non-contact based. Several anti-spoofing methods have been proposed in the literature for both contact and non-contact based biometric modalities often using video to study the temporal characteristics of a real vs. spoofed biometric signal. This paper presents a novel multi-feature evidence aggregation method for face spoofing detection. The proposed method fuses evidence from features encoding of both texture and motion (liveness) properties in the face and also the surrounding scene regions. The feature extraction algorithms are based on a configuration of local binary pattern and motion estimation using histogram of oriented optical flow. Furthermore, the multi-feature windowed videolet aggregation of these orthogonal features coupled with support vector machine-based classification provides robustness to different attacks. We demonstrate the efficacy of the proposed approach by evaluating on three standard public databases: CASIA-FASD, 3DMAD and MSU-MFSD with equal error rate of 3.14%, 0%, and 0%, respectively.read more
Citations
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Proceedings ArticleDOI
Face anti-spoofing using patch and depth-based CNNs
TL;DR: A novel two-stream CNN-based approach for face anti-spoofing is proposed, by extracting the local features and holistic depth maps from the face images, which facilitate CNN to discriminate the spoof patches independent of the spatial face areas.
Proceedings ArticleDOI
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
TL;DR: Yu et al. as discussed by the authors proposed a frame level FAS method based on Central Difference Convolution (CDC), which is able to capture intrinsic detailed patterns via aggregating both intensity and gradient information.
Book ChapterDOI
Face De-Spoofing: Anti-Spoofing via Noise Modeling
TL;DR: A CNN architecture with proper constraints and supervisions is proposed to overcome the problem of having no ground truth for the decomposition of face de-spoofing, and the results show promising improvements due to the spoof noise modeling.
Journal ArticleDOI
A comprehensive overview of biometric fusion
TL;DR: A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is presented in this paper, where the authors provide a comprehensive overview of the role of information fusion in biometrics.
Journal ArticleDOI
Detecting Silicone Mask-Based Presentation Attack via Deep Dictionary Learning
TL;DR: This paper introduces the first-of-its-kind silicone mask attack database which contains 130 real and attacked videos to facilitate research in developing presentation attack detection algorithms for this challenging scenario.
References
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Journal ArticleDOI
Face spoofing detection from single images using texture and local shape analysis
TL;DR: The authors present a novel approach based on analysing facial image for detecting whether there is a live person in front of the camera or a face print, using a set of low-level feature descriptors, fast linear classification scheme and score level fusion.
Proceedings ArticleDOI
Face Anti-spoofing Based on General Image Quality Assessment
Javier Galbally,Sébastien Marcel +1 more
TL;DR: The findings presented in the work clearly suggest that the analysis of the general image quality of real face samples reveals highly valuable information that may be very efficiently used to discriminate them from fake images.
Proceedings ArticleDOI
Complementary countermeasures for detecting scenic face spoofing attacks
TL;DR: This paper provides an intuitive way to explore the fusion potential of different visual cues and shows that the performance of the individual methods can be vastly improved by performing fusion at score level, and questions the idea of using complex classification schemes in individual countermeasures.
Proceedings ArticleDOI
The 2nd competition on counter measures to 2D face spoofing attacks
Ivana Chingovska,Jianwei Yang,Zhen Lei,Dong Yi,Stan Z. Li,O. Kahm,C. Glaser,Naser Damer,Arjan Kuijper,Alexander Nouak,Jukka Komulainen,Tiago de Freitas Pereira,Shubham Gupta,Shubham Khandelwal,Shubham Bansal,Ayush K. Rai,Tarun Krishna,Dushyant Goyal,Muhammad Adeel Waris,Honglei Zhang,Iftikhar Ahmad,Serkan Kiranyaz,Moncef Gabbouj,Roberto Tronci,Maurizio Pili,Nicola Sirena,Fabio Roli,Javier Galbally,J. Ficrrcz,Allan Pinto,Helio Pedrini,W. S. Schwartz,Anderson Rocha,André Anjos,Sébastien Marcel +34 more
TL;DR: The objective of the 2nd Competition on Counter Measures to 2D Face Spoofing Attacks is to challenge researchers to create counter measures effectively detecting a variety of attacks.
Proceedings ArticleDOI
Live face video vs. spoof face video: Use of moiré patterns to detect replay video attacks
TL;DR: This work addresses the problem of facial spoofing detection against replay attacks based on the analysis of aliasing in spoof face videos and shows that the proposed approach is very effective in face spoof detection for both cross-database, and intra-database testing scenarios.