A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing

Nagpal, Chaitanya; Dubey, Shiv Ram

doi:10.48550/arxiv.1805.04176

Cited by 5 publications

(8 citation statements)

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“…Since then, CNN has achieved great breakthrough with the help of hardware development and data abundance. Recently, CNN is also widely used in face anti-spoofing tasks [21,23,14,12,19,25,35]. However, most of the deep learning methods simply consider face anti-spoofing as a binary classification problem with softmax loss.…”

Section: Related Workmentioning

confidence: 99%

“…Both [19] and [25] fine-tune a pre-trained VGG-face model and take it as a feature extractor for the subsequent classification. Nagpal et al [23] comprehensively study the influence of different network architectures and hyperparameters on face anti-spoofing. Feng [12] and Li [19] feed different kinds of face images into the CNN network to learn discriminative features on living faces and spoofing faces.…”

Section: Related Workmentioning

confidence: 99%

“…To defend against face spoofing attacks, a great number of face anti-spoofing methods [5,14,21,23] have been proposed to discriminate the living and fake face. Previous approaches fall into two categories.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to texturebased methods, CNN-based methods provide an alternative way to learn discriminative anti-spoofing representations in an end-to-end fashion. Some CNN-based methods [21,23,14,34,35,30] only treat face anti-spoofing as a pure binary classification -spoofing as 0 and living as 1, and train the neural network supervised by simple softmax loss. However, these methods fail to explore the nature of spoof patterns [20], which consist of skin detail loss, color distortion, moiré pattern, motion pattern, shape deformation and spoofing artifacts.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting temporal and depth information for multi-frame face anti-spoofing

Wang,

Zhao,

Qin

et al. 2018

Preprint

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Face anti-spoofing is significant to the security of face recognition systems. Previous works on depth supervised learning have proved the effectiveness for face antispoofing. Nevertheless, they only considered the depth as an auxiliary supervision in the single frame. Different from these methods, we develop a new method to estimate depth information from multiple RGB frames and propose a depth-supervised architecture which can efficiently encodes spatiotemporal information for presentation attack detection. It includes two novel modules: optical flow guided feature block (OFFB) and convolution gated recurrent units (ConvGRU) module, which are designed to extract short-term and long-term motion to discriminate living and spoofing faces. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art results on four benchmark datasets, namely OULU-NPU, SiW, CASIA-MFSD, and Replay-Attack.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploiting temporal and depth information for multi-frame face anti-spoofing

Wang,

Zhao,

Qin

et al. 2018

Preprint

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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

Preprint

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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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Dual CNN Models for Unsupervised Monocular Depth Estimation

Repala

Dubey

2019

Lecture Notes in Computer Science

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The unsupervised depth estimation is the recent trend by utilizing the binocular stereo images to get rid of depth map ground truth. In unsupervised depth computation, the disparity images are generated by training the CNN with an image reconstruction loss. In this paper, a dual CNN based model is presented for unsupervised depth estimation with 6 losses (DNM6) with individual CNN for each view to generate the corresponding disparity map. The proposed dual CNN model is also extended with 12 losses (DNM12) by utilizing the cross disparities. The presented DNM6 and DNM12 models are experimented over KITTI driving and Cityscapes urban database and compared with the recent state-of-the-art result of unsupervised depth estimation. The code is available at: https://github.com/ishmav16/Dual-CNN-Models-for-Unsupervised-Monocular-Depth-Estimation.

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A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing

Cited by 5 publications

References 0 publications

Exploiting temporal and depth information for multi-frame face anti-spoofing

Exploiting temporal and depth information for multi-frame face anti-spoofing

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

Dual CNN Models for Unsupervised Monocular Depth Estimation

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