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
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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Journal ArticleDOI
Face spoofing detection via ensemble of classifiers toward low-power devices
TL;DR: This work contemplates low-power devices through the aggregation of Fourier transforms, different classification methods and handcrafted descriptors to estimate whether face samples correspond to falsification attacks, and is one of the few methods associating features derived from both spatial and frequency image domains.
Posted Content
On the Robustness of Face Recognition Algorithms Against Attacks and Bias
TL;DR: In this paper, different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed.
Posted Content
Exploring Hypergraph Representation on Face Anti-spoofing Beyond 2D Attacks
TL;DR: Zhang et al. as mentioned in this paper proposed Hypergraph Convolutional Neural Networks (HGCNN) for 3D face anti-spoofing via hypergraph convolutional neural networks.
Book ChapterDOI
Perturbing Convolutional Feature Maps with Histogram of Oriented Gradients for Face Liveness Detection.
TL;DR: Experimental results indicate that the proposed framework perform significantly better compare to previous state-of-the-art approaches in both intra-database and cross-database face anti-spoofing scenarios.
Proceedings ArticleDOI
MixNet for Generalized Face Presentation Attack Detection
TL;DR: In this article, the authors proposed a deep learning-based network termed as MixNet to detect presentation attacks in cross-database and unseen attack settings, which utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category.
References
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Face Spoof Detection With Image Distortion Analysis
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TL;DR: An efficient and rather robust face spoof detection algorithm based on image distortion analysis (IDA) that outperforms the state-of-the-art methods in spoof detection and highlights the difficulty in separating genuine and spoof faces, especially in cross-database and cross-device scenarios.
Proceedings Article
On the effectiveness of local binary patterns in face anti-spoofing
TL;DR: This paper inspects the potential of texture features based on Local Binary Patterns (LBP) and their variations on three types of attacks: printed photographs, and photos and videos displayed on electronic screens of different sizes and concludes that LBP show moderate discriminability when confronted with a wide set of attack types.