Auto-Fas: Searching Lightweight Networks for Face Anti-Spoofing

Yu, Zitong; Qin, Yunxiao; Xu, Xiaqing; Zhao, Chenxu; Wang, Zezheng; Lei, Zhen; Zhao, Guoying

doi:10.1109/icassp40776.2020.9053587

Cited by 41 publications

(24 citation statements)

References 14 publications

(20 reference statements)

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“…In recent years, several hand-crafted features based [7,8,15,29,45,44] and deep learning based [49,64,36,26,62,4,19,20] methods have been proposed for presentation attack detection (PAD). On one hand, the classical handcrafted descriptors (e.g., local binary pattern (LBP) [7]) leverage local relationship among the neighbours as the discriminative features, which is robust for describing the detailed invariant information (e.g., color texture, moiré pattern and noise artifacts) between the living and spoofing faces.…”

Section: Introductionmentioning

confidence: 99%

Searching Central Difference Convolutional Networks for Face Anti-Spoofing

Zhao

Wang³

et al. 2020

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

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435

300

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Face anti-spoofing (FAS) plays a vital role in face recognition systems. Most state-of-the-art FAS methods 1) rely on stacked convolutions and expert-designed network, which is weak in describing detailed fine-grained information and easily being ineffective when the environment varies (e.g., different illumination), and 2) prefer to use long sequence as input to extract dynamic features, making them difficult to deploy into scenarios which need quick response. Here we propose a novel 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. A network built with CDC, called the Central Difference Convolutional Network (CDCN), is able to provide more robust modeling capacity than its counterpart built with vanilla convolution. Furthermore, over a specifically designed CDC search space, Neural Architecture Search (NAS) is utilized to discover a more powerful network structure (CDCN++), which can be assembled with Multiscale Attention Fusion Module (MAFM) for further boosting performance. Comprehensive experiments are performed on six benchmark datasets to show that 1) the proposed method not only achieves superior performance on intra-dataset testing (especially 0.2% ACER in Protocol-1 of OULU-NPU dataset), 2) it also generalizes well on cross-dataset testing (particularly 6.5% HTER from CASIA-MFSD to Replay-Attack datasets). The codes are available at https://github.com/ZitongYu/CDCN.

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Section: Introductionmentioning

confidence: 99%

Searching Central Difference Convolutional Networks for Face Anti-Spoofing

Zhao

Wang³

et al. 2020

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

Self Cite

435

300

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“…However, most of existing face recognition systems are easily to be spoofed through presentation attacks (PAs) ranging from printing a face on paper (print attack) to replaying a face on a digital device (replay attack) or bringing a 3D-mask (3D-mask attack). 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.…”

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)

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198

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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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“…In recent years, several hand-crafted feature based [3,4,7,15,28,27] and deep learning based [38,33,29,22,12,34,2,8,9] methods have been proposed for presentation attack detection (PAD). On one hand, the classical hand- * denotes corresponding author [21].…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Face Anti-Spoofing Based on Central Difference Networks

Qin

et al. 2020

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

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Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Existing multi-modal FAS methods rely on stacked vanilla convolutions, which is weak in describing detailed intrinsic information from modalities and easily being ineffective when the domain shifts (e.g., cross attack and cross ethnicity). In this paper, we extend the central difference convolutional networks (CDCN) [39] to a multi-modal version, intending to capture intrinsic spoofing patterns among three modalities (RGB, depth and infrared). Meanwhile, we also give an elaborate study about single-modal based CDCN. Our approach won the first place in Track Multi-Modal as well as the second place in Track Single-Modal (RGB) of ChaLearn Face Anti-spoofing Attack Detection Challenge@CVPR2020 [20]. Our final submission obtains 1.02±0.59% and 4.84±1.79% ACER in Track Multi-Modal and Track Single-Modal (RGB), respectively. The codes are available at https://github.com/ZitongYu/CDCN.

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Auto-Fas: Searching Lightweight Networks for Face Anti-Spoofing

Cited by 41 publications

References 14 publications

Searching Central Difference Convolutional Networks for Face Anti-Spoofing

Searching Central Difference Convolutional Networks for Face Anti-Spoofing

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

Multi-Modal Face Anti-Spoofing Based on Central Difference Networks

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