Discriminative representation combinations for accurate face spoofing detection

Song, Xiaoning; Zhao, Xu; Fang, Liangji; Lin, Tianwei

doi:10.1016/j.patcog.2018.08.019

Cited by 61 publications

(25 citation statements)

References 51 publications

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“…Therefore, not only the research community but also the industry has recognized face anti-spoofing [18,19,4,33,39,11,23,55,1,29,12,49,45,54,21] as a critical role in securing the face recognition system. In the past few years, both traditional methods [14,42,9] and CNN-based methods [35,38,20,24,46] have shown effectiveness in discriminating between the living and spoofing face. They often formalize face anti-spoofing as a binary classification between spoofing and living images.…”

Section: Introductionmentioning

confidence: 99%

Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing

Wang¹,

Zhao

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

183

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Face anti-spoofing is critical to the security of face recognition systems. Depth supervised learning has been proven as one of the most effective methods for face antispoofing. Despite the great success, most previous works still formulate the problem as a single-frame multi-task one by simply augmenting the loss with depth, while neglecting the detailed fine-grained information and the interplay between facial depths and moving patterns. In contrast, we design a new approach to detect presentation attacks from multiple frames based on two insights: 1) detailed discriminative clues (e.g., spatial gradient magnitude) between living and spoofing face may be discarded through stacked vanilla convolutions, and 2) the dynamics of 3D moving faces provide important clues in detecting the spoofing faces. The proposed method is able to capture discriminative details via Residual Spatial Gradient Block (RSGB) and encode spatio-temporal information from Spatio-Temporal Propagation Module (STPM) efficiently. Moreover, a novel Contrastive Depth Loss is presented for more accurate depth supervision. To assess the efficacy of our method, we also collect a Double-modal Anti-spoofing Dataset (DMAD) which provides actual depth for each sample. The experiments demonstrate that the proposed approach achieves state-of-the-art results on five benchmark datasets including OULU-NPU, SiW, CASIA-MFSD, Replay-Attack, and the new DMAD. Codes will be available at https://github.com/clks-wzz/ FAS-SGTD.

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Section: Introductionmentioning

confidence: 99%

Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing

Wang¹,

Zhao

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

183

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show abstract

“…In contrast, the last two approaches analysed [87,90] outperform our technique by one order of magnitude for the CASIA database. However, our best result is three time better than those reported in Song et al [87] for REPLAY-ATTACK (i.e. 0.00% vs. 0.03%).…”

Section: Benchmark With State Of the Artmentioning

confidence: 79%

On the generalisation capabilities of Fisher vector‐based face presentation attack detection

2021

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In past decades, the broad development experienced by biometric systems has unveiled several threats that may decrease their trustworthiness. These are attack presentations that can be easily carried out by a non‐authorised subject to gain access to the biometric system. To mitigate those security concerns, most face presentation attack detection techniques have reported good detection performance when they are evaluated on known presentation attack instruments (PAIs) and under acquisition conditions. In contrast, for more realistic scenarios, existing algorithms face difficulties in detecting unknown PAI species which are only included in the test set. A feature space based on Fisher Vectors computed from compact binarised statistical image features histograms, which allows discovering semantic feature subsets from known samples to enhance the detection of unknown attacks is presented. This representation, which is evaluated for challenging unknown attacks taken from freely available facial databases, reports promising results. A Bona Fide Presentation Classification Error Rate 100 under 17% together with an area under the receiving operating characteristic curve of over 98% can be achieved in the presence of unknown attacks. In addition, by training a limited number of parameters, the method is able to achieve state‐of‐the‐art deep learning‐based approaches for cross‐dataset scenarios.

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“…After extracting valid features, an efficient and accurate classifier is supposed to design. Various classifiers are adopted in face presentation attack detection (see [12,[42][43][44], for instance). In general, the monotone classifier structure equipped with fixed parameters possibly leads to the deviation of classification results.…”

Section: Design Of the Classifiermentioning

confidence: 99%

Towards Face Presentation Attack Detection Based on Residual Color Texture Representation

Qiao

et al. 2021

Security and Communication Networks

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Most existing face authentication systems have limitations when facing the challenge raised by presentation attacks, which probably leads to some dangerous activities when using facial unlocking for smart device, facial access to control system, and face scan payment. Accordingly, as a security guarantee to prevent the face authentication from being attacked, the study of face presentation attack detection is developed in this community. In this work, a face presentation attack detector is designed based on residual color texture representation (RCTR). Existing methods lack of effective data preprocessing, and we propose to adopt DW-filter for obtaining residual image, which can effectively improve the detection efficiency. Subsequently, powerful CM texture descriptor is introduced, which performs better than widely used descriptors such as LBP or LPQ. Additionally, representative texture features are extracted from not only RGB space but also more discriminative color spaces such as HSV, YCbCr, and CIE 1976 L∗a∗b (LAB). Meanwhile, the RCTR is fed into the well-designed classifier. Specifically, we compare and analyze the performance of advanced classifiers, among which an ensemble classifier based on a probabilistic voting decision is our optimal choice. Extensive experimental results empirically verify the proposed face presentation attack detector’s superior performance both in the cases of intradataset and interdataset (mismatched training-testing samples) evaluation.

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Discriminative representation combinations for accurate face spoofing detection

Cited by 61 publications

References 51 publications

Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing

Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing

On the generalisation capabilities of Fisher vector‐based face presentation attack detection

Towards Face Presentation Attack Detection Based on Residual Color Texture Representation

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