computer-vision-11

Face Presentation Attack Detection

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection

Anjith George, S. Marcel•Fri May 31 2019

A Convolutional Neural Network (CNN) based framework for presentation attack detection, with deep pixel-wise supervision, suitable for deployment in smart devices with minimal computational and time overhead is introduced.

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Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks

Anjith George, S. Marcel•Tue Jul 21 2020

A new framework for PAD is proposed using a one-class classifier, where the representation used is learned with a Multi-Channel Convolutional Neural Network (MCCNN) and a novel loss function is introduced, which forces the network to learn a compact embedding for bonafide class while being far from the representation of attacks.

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Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection

P. Yuen, Xiangyuan Lan, Rui Shao, Jiawei Li•Fri May 31 2019

Face presentation attacks have become an increasingly critical issue in the face recognition community. Many face anti-spoofing methods have been proposed, but they cannot generalize well on "unseen" attacks. This work focuses on improving the generalization ability of face anti-spoofing methods from the perspective of the domain generalization. We propose to learn a generalized feature space via a novel multi-adversarial discriminative deep domain generalization framework. In this framework, a multi-adversarial deep domain generalization is performed under a dual-force triplet-mining constraint. This ensures that the learned feature space is discriminative and shared by multiple source domains, and thus is more generalized to new face presentation attacks. An auxiliary face depth supervision is incorporated to further enhance the generalization ability. Extensive experiments on four public datasets validate the effectiveness of the proposed method.

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Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing

Anjith George, Olegs Nikisins, S. Marcel•Fri May 31 2019

A novel Autoencoders + MLP based face PAD algorithm is developed, and it is demonstrated, that learning the features of individual facial regions, is more discriminative than the features learned from an entire face.

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Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network

Anjith George, Z. Mostaani, David Geissenbuhler, Olegs Nikisins, André Anjos, S. Marcel•Wed Sep 18 2019

A multi-channel Convolutional Neural Network-based approach for presentation attack detection (PAD) and the new Wide Multi-Channel presentation Attack (WMCA) database for face PAD which contains a wide variety of 2D and 3D presentation attacks for both impersonation and obfuscation attacks is introduced.

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Specular- and Diffuse-reflection-based Face Spoofing Detection for Mobile Devices

Akinori F. Ebihara, K. Sakurai, Hitoshi Imaoka•Sun Sep 27 2020

In light of the rising demand for biometric-authentication systems, preventing face spoofing attacks is a critical issue for the safe deployment of face recognition systems. Here, we propose an efficient face presentation attack detection (PAD) algorithm that requires minimal hardware and only a small database, making it suitable for resource-constrained devices such as mobile phones. Utilizing one monocular visible light camera, the proposed algorithm takes two facial photos, one taken with a flash, the other without a flash. The proposed SpecDiff descriptor is constructed by leveraging two types of reflection: (i) specular reflections from the iris region that have a specific intensity distribution depending on liveness, and (ii) diffuse reflections from the entire face region that represents the 3D structure of a subject's face. Classifiers trained with SpecDiff descriptor outperforms other flash-based PAD algorithms on both an in-house database and on publicly available NUAA, Replay-Attack, and SiW databases. Moreover, the proposed algorithm achieves statistically significantly better accuracy to that of an end-to-end, deep neural network classifier, while being approximately six-times faster execution speed. The code is publicly available at https://github.com/Akinori-F-Ebihara/SpecDiff-spoofing-detector.

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Anomaly Detection-Based Unknown Face Presentation Attack Detection

Vishal M. Patel, Poojan Oza, Pramuditha Perera, Yashasvi Baweja•Fri Jul 10 2020

A deep-learning solution for anomaly detection-based spoof attack detection where both classifier and feature representations are learned together end-to-end, which benefits from the representation learning power of the CNNs and learns better features for fPAD task as shown in the ablation study.

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MixNet for Generalized Face Presentation Attack Detection

Akshay Agarwal, Richa Singh, Mayank Vatsa, Nilay Sanghvi•Sat Oct 24 2020

This research has proposed a deep learning-based network termed as MixNet to detect presentation attacks in cross-database and unseen attack settings and shows the effectiveness of the proposed algorithm.

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Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detection

Naser Damer, Arjan Kuijper, Meiling Fang, Florian Kirchbuchner•Wed Sep 15 2021

This work proposes a dual-stream convolution neural networks framework and proposes a hierarchical attention module integration to join the information from the two streams at different stages by considering the nature of deep features in different layers of the CNN.

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