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PAD#xff08;presentation attack detection#xff09;
动作配合式活体检测#xff1a;给出指定动作要求#xff0c;用户需配合完成#xff0c;通过实时检测用户眼睛#xff0c;嘴巴#xff0c;头部姿态的状态#xff0c;来判断是否是活体。H5视频活体检…活体检测
PADpresentation attack detection
动作配合式活体检测给出指定动作要求用户需配合完成通过实时检测用户眼睛嘴巴头部姿态的状态来判断是否是活体。H5视频活体检测用户上传一个现场录制的视频录制时读出随机分配的语音校验码。然后通过分析这个视频的人脸信息以及语音校验码是否匹配完成活体检测判断。静默活体检测相对于动态活体检测方法静默活体检测是指不需要用户做任何动作自然面对摄像头3、4秒钟即可。由于真实人脸并不是绝对静止的 存在微表情如眼皮眼球的律动、眨眼、嘴唇及周边面颊的伸缩等可通过此类特征反欺骗。图片活体检测基于图片中人像的破绽摩尔纹、成像畸形等来判断目标对象是否为活体可有效防止屏幕二次翻拍等作弊攻击可使用单张或多张判断逻辑。近红外活体检测利用近红外成像原理实现夜间或无自然光条件下的活体判断。其成像特点如屏幕无法成像不同材质反射率不同等可以实现高鲁棒性的活体判断。3D结构光活体检测基于3D结构光成像原理通过人脸表面反射光线构建深度图像判断目标对象是否为活体可强效防御图片、视频、屏幕、模具等攻击。光流法利用图像序列中的像素强度数据的时域变化和相关性来确定各自像素位置的“运动”从图像序列中得到各个像素点的运行信息采用高斯差分滤波器、LBP特征和支持向量机进行数据统计分析。同时光流场对物体运动比较敏感利用光流场可以统一检测眼球移动和眨眼。这种活体检测方式可以在用户无配合的情况下实现盲测。
传统方法
1镜面反射图像质量失真颜色 specular reflection, blurriness features, chromatic moment and color diversity Di Wen, Hu Han, Anil K. Jain. Face Spoof Detection with Image Distortion Analysis. IEEE Transactions on Information Forensics and Security, 2015 2HSV空间人脸多级LBP特征 YCbCr空间人脸LPQ特征 Zinelabidine Boulkenafet, Jukka Komulainen, Abdenour Hadid. Face Spoofing Detection Using Colour Texture Analysis. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2016 3捕获活体与非活体微动作之间的差异来设计特征一个是先通过运动放大来增强脸部微动作 然后提取方向光流直方图HOOF 动态纹理LBP-TOP 特征一个是通过动态模式分解DMD得到最大运动能量的子空间图再分析纹理。 Santosh Tirunagari, Norman Poh. Detection of Face Spoofing Using Visual Dynamics. IEEE TRANS. ON INFORMATION FORENSICS AND SECURIT, 2015 4先通过 pluse 在频域上分布不同先区分活体 or 照片攻击 因为照片中的人脸提取的心率分布不同再若判别1结果是活体再 cascade 一个纹理LBP分类器来区分活体 or 屏幕攻击因为屏幕视频中人脸心率分布与活体相近 Xiaobai Li, , Guoying Zhao. Generalized face anti-spoofing by detecting pulse from face videos, 2016 23rd ICPR 5 multi-scale local binary pattern (LBP) followed by a non-linear SVMtexture J. Määttä, A. Hadid, and M. Pietikäinen, “Face spoofing detection from single images using micro-texture analysis,” in Biometrics (IJCB), 2011 international joint conference on. IEEE, 2011, pp. 1–7. 6 used the LBP-TOP features containing space and time descriptors to encode the motion information along with the face texture T. de Freitas Pereira, A. Anjos, J. M. De Martino, and S. Marcel, “Lbp- top based countermeasure against face spoofing attacks,” in Asian Conference on Computer Vision. Springer, 2012, pp. 121–132. 7multiple Difference of Gaussian (DoG) filters to remove the noise and low-frequency information. They used the high frequency information to generate the feature vector for SVM classifier Z. Zhang, J. Yan, S. Liu, Z. Lei, D. Yi, and S. Z. Li, “A face antispoofing database with diverse attacks,” in Biometrics (ICB), 2012 5th IAPR international conference on. IEEE, 2012, pp. 26–31. 8the motion relation between foreground and background A. Anjos and S. Marcel, “Counter-measures to photo attacks in face recognition: a public database and a baseline,” in Biometrics (IJCB),2011 international joint conference on. IEEE, 2011, pp. 1–7. 9(2015)FACE ANTI-SPOOFING BASED ON COLOR TEXTURE ANALYSIS
深度学习方法论文概要
1 (2018)Discriminative Representation Combinations for Accurate Face Spoofing Detection SPMT spatial pyramid coding micro-texture local SSD Single Shot MultiBox Detectorcontext TFBD template face matched binocular depthstereo 1SSDSPMT one image 2TFBDSPMTbinocular image pair 把 活体检测 直接放到 人脸检测SSDMTCNN等 模块里作为一个类即人脸检测出来的 bbox 里有 背景真人人脸假人脸 三类的置信度这样可以在早期就过滤掉一部分非活体。
2 (2018)Learning Deep Models for Face Anti-Spoofing Binary or Auxiliary Supervision 二分类with softmax 只学到训练集的某一种区分特征黑盒而且泛化性不强。 提出auxillary supervision提取时间和空间信息face depthpixel-wise、CNN和rPPG signalssequence-wise、RNN。 设计了深度框架准端到端地去预测 Pulse统计量 及 Depth map 这里说的“准”就是最后没接分类器直接通过样本 feature 的相似距离阈值决策 1过去方法把活体检测看成二分类问题直接让DNN去学习这样学出来的cues不够general 和 discriminative 2将二分类问题换成带目标性地特征监督问题即 回归出 pulse 统计量 回归出 Depth map保证网络学习的就是这两种特征哈哈不排除假设学到了 color texture 在里面黑箱网络这么聪明 回归 Depth map就是通过 Landmark 然后 3DMMfitting 得到 人脸3D shape然后再阈值化去背景得到 depth map 的 groundtruth最后和网络预测的 estimated depth map 有 L2 loss。 而文章亮点在于设计了 Non-rigid Registration Layer 来对齐各帧人脸的非刚性运动如姿态表情等然后通过RNN更好地学到 temporal pulse 信息。 为什么需要这个对齐网络呢我们来想想在做运动识别任务时只需简单把 sampling或者连续帧 合并起来喂进网络就行了是假定相机是不动的对象在运动而文中需要对连续人脸帧进行pulse特征提取主要对象是人脸上对应ROI在 temporal 上的 Intensity 变化所以就需要把人脸当成是相机固定不动。
3 (2018)Face De-Spoofing Anti-Spoofing via Noise Modeling 假设噪音是ubiquitous and repetive。 单帧方法启发于图像去噪denoise 图像去抖动 deblur无论是噪声图还是模糊图都可看成是在原图上加噪声运算或者模糊运算而去噪和去抖动就是估计噪声分布和模糊核从而重构回原图。文中把活体人脸图看成是原图 而非活体人脸图看成是加了噪声后失真的 x 故 task 就变成估计 Spoof noise 然后用这个 Noise pattern feature 去分类决策。 那问题来了数据集没有像素级别一一对应的 groundtruth也没有Spoof Noise模型的先验知识如果有知道Noise模型可以用Live Face来生成Spoofing Face那拿什么来当groundtruth怎么设计网络去估计 Spoofing noise 呢 如一般Low-level image 任务一样文中利用Encoder-decoder来得到 Spoof noise N然后通过残差重构 这就是下图的DS Net。为了保证网络对于不同输入学出来的Noise是有效的根据先验知识设计了三个Loss来constrain 1Magnitude loss(当输入是Live face时N尽量逼近0) 2Repetitive loss(Spooing face的Noise图在高频段有较大的峰值) 3Map Loss(让Real Face 的 deep feature map分布尽量逼近全0而Spoofing face的 deep feature map 尽量逼近全1) 那网络右边的 VQ-Net 和 DQ-Net 又有什么作用呢因为没有 Live face 的 Groundtruth要保证重构出来的分布接近 Live face作者用了对抗生成网络GAN (即 VQ-Net )去约束重构生成的live face 与Live face分布尽量一致而用了 pre-trained Depth model 来保证结果live face的深度图与Live face的深度图尽量一致。 Pros: 通过可视化最终让大众知道了 Spoofing Noise 是长什么样子的~ Cons: 在实际场景中难部署该模型假定Spoofing Noise是 strongly 存在的当实际场景中活体的人脸图质量并不是很高而非活体攻击的质量相对高时Spoofing noise走不通
**4 (2019)A Performance Evaluation of Convolutional Neural ** 测试多种核心CNN网络、是否transfer、是否init random、learning rate The face anti-spoofing is considered as the two-class classification problem in this paper. The two classes are real face class and spoofed face class. the CNN model predicts the class score for training images, computes the categorical cross-entropy loss TABLE : The training, validation and testing performance comparison among Inception-v3, ResNet50 and ResNet152 models in terms of the accuracy, convergence rate, and varying parameters like initial weights, number of trainable layers and learning rate. In this table, the ‘Epochs’ is the number of epochs for highest validation accuracy.
5 (2019)Deep Transfer Across Domains for Face Anti-spoofing 提出现有的方法泛化性都不足。原因 1the variety of spoofing materials can make the spoofing attacks quite different. 2limited labeled data is available for training in face anti-spoofing. We propose to learn a shared feature subspace where the distributions of the real access samples (genuine) from different domains, and the distributions of different types of spoofing attacks (fake) from different domains are drawn close, respectively. In the proposed framework, the sufficient labeled source data are used to learn discriminative representations that distinguish the genuine samples and the fake samples, meanwhile the sparsely labeled target samples are fed to the network to calculate the feature distribution distance between the genuine samples from the source and the target domain, and between the fake samples from the source and the target domains, corresponding to their materials. The kernel approach is adopted to map the features output from the CNN into a common kernel space, and the Maximum Mean Discrepancy (MMD) is adopted to measure the distribution distance between the samples from the source and target domains. This feature distribution distance is treated as a domain loss term added to the objective function and minimized along with training of the network. Figure : The flowchart of the proposed framework, where every input batch contains half the source images and half the target images. Features of the two domains output from the last pooling layer are used to calculate the distribution distance with kernel based MMD. The network is trained using the classification loss along with the distribution distance which is taken as domain loss.
6 (2018)Deep Tree Learning for Zero-shot Face Anti-Spoofing the detection of unknown spoof attacks as Zero-Shot Face Anti-spoofing (ZSFA).A novel Deep Tree Network (DTN) is proposed to partition the spoof samples into semantic sub-groups in an unsupervised fashion.Assuming there are both homogeneous features among different spoof types and distinct features within each spoof type, a tree-like model is well-suited to handle this case: learning the homogeneous features in the early tree nodes and distinct features in later tree nodes. Figure : The proposed Deep Tree Network (DTN) architecture. (a) the overall structure of DTN. A tree node consists of a Convolutional Residual Unit (CRU) and a Tree Routing Unit (TRU), and a leaf node consists of a CRU and a Supervised Feature Learning (SFL) module. (b) the concept of Tree Routing Unit (TRU): finding the base with largest variations; © the structure of each Convolutional Residual Unit(CRU); (d) the structure of the Supervised Feature Learning (SFL) in the leaf nodes.
7 (2019)Enhance the Motion Cues for Face Anti-Spoofing using fine-grained motions比如眨眼、手抖 Extract the high discriminative features of video frames using the conventional Convolutional Neural Network (CNN). Then we leverage Long Short-Term Memory (LSTM) with the extracted features as inputs to capture the temporal dynamics in videos.To ensure the fine-grained motions more easily to be perceived in the training process, the eulerian motion magnification is used as the preprocessing to enhance the facial expressions exhibited by individuals, and the attention mechanism is embedded in LSTM to ensure the model learn to focus selectively on the dynamic frames across the video clips. Fig: (a) The flowchart of the proposed CNN-LSTM framework. (b) The cascaded LSTM architecture. © Illustration of a single LSTM unit, the current state t depends on the past state t 1 of the same neuron.
8 (2019)FeatherNets Convolutional Neural Networks as Light as Feather 提出一种非常小的网络结构fixes the weakness of Global Average Pooling–Streaming Module use depth image only( the depth information is estimated from RGB image) “ensemble cascade” structure Figure. Streaming Module. The last blocks’ output is down-sampled by a depthwise convolution[28, 29] with stride larger than 1 and flattened directly into an one-dimensional vector. Figure. Multi-Modal Fusion Strategy: Two stages cascaded, stage 1 is an ensemble classifier consisting of several depth models. Stage 2 employs IR models to classify the uncertain samples from stage 1.
9 (2018)LiveNet Improving features generalization for face liveness detection continuous data-randomization (like bootstrapping) Fig. The sampling is done in the form of mini-batches. (a) Conventional method for training CNN Networks. (b) Proposed method for training CNN networks.
10(2019)Learning Generalizable and Identity-Discriminative Representations for face anti-spoofing 1Total Pairwise Confusion(TPC) loss 2Fast Domain Adaptation(FDA) component into the CNN model to alleviate negative effects brought by domain changes 3Generalizable Face Authentication CNN model,works in a multi-task manner, performing simultaneously face anti-spoofing and face recognition Figure : Architecture of proposed GFA-CNN. The whole network contains two branches. The face anti-spoofing branch (upper) takes as input the domain-adaptive images transferred by FDA and optimized by TPC-loss and Anti-loss, while the face recognition branch (bottom) takes the cropped face images as input and is trained by minimizing Recog-loss. The structure settings are shown on top of each block, where “ID number” indicates the number of subjects involved in training. The two branches share parameters during training.
11(2019)Improving Face Anti-Spoofing by 3D Virtual Synthesis 合成更多的spoof 数据
12( 2019)Generalized Presentation Attack Detection a face anti-spoofing evaluation proposal 1proposed a framework, GRAD-GPAD, for systematic evaluation of the generalization properties of face-PAD methods 2提出了两种新的评估协议Cross-FaceResolution、Cross-Conditions 原有的协议Grandtest、Cross-Dataset、One-PAI、Unseen Attacks (Cross-PAI)、 Unseen Capture Devices:
13( 2019)Exploiting temporal and depth information for multi-frame face anti-spoofing estimate depth information from multiple RGB frames Figure . The pipeline of proposed architecture. The inputs are consecutive frames in a fixed interval. Our single-frame part aims to extract features at various levels and to output the single-frame estimated facial depth. OFF blocks take single-frame features from two consecutive frames as inputs and calculate short-term motion features. Then the final OFF features are fed into the ConvGRUs to obtain long-term motion information, and output the residual of single-frame facial depth. Finally, the combined estimated multi-frame depth maps are supervised by the depth loss and binary loss in respective manners.
14( 2019)Meta Anti-spoofing: Learning to Learn in Face Anti-spoofing a few-shot learning problem with evolving new attacks Figure. (a) Network structure of Meta-FAS-CS which aims to train a meta-learner through classification label. (b) Network structure of Meta-FAS-DR which aims to train a meta-learner through depth label.
15( 2019)Deep Anomaly Detection for Generalized Face Anti-Spoofing Figure : We propose a deep metric learning approach, using a set of Siamese CNNs, in conjunction with the combination of a triplet focal loss and a novel “metric softmax” loss. The latter accumulates the probability distribution of each pair within the triplet. Our aim is to learn a feature representation that allows us to detect impostor samples as anomalies.
16( 2019)Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection extracts the normal cues via light reflection analysis, and then uses an end-to-end trainable multi-task Convolutional Neural Network (CNN) to not only recover subjects’ depth maps to assist liveness classification, but also provide the light CAPTCHA checking mechanism in the regression branch to further improve the system reliability Figure : Overview of Aurora Guard. From facial reflection frames encoded by casted light CAPTCHA, we estimate the normal cues. In the classification branch, we recover the depth maps from the normal cues, and then perform depth-based liveness classification. In the regression branch, we obtain the estimated light CAPTCHA.
17( 2019)Towards Real-time Eyeblink Detection in The WildDataset, Theory and Practices After locating and tracking human eye using SeetaFace engine and KCF tracker respectively, a modified LSTM model able to capture the multi-scale temporal information is proposed to execute eyeblink verification.A feature extraction approach that reveals appearance and motion characteristics simultaneously is also proposed.
18( 2018)Exploring Hypergraph Representation on Face Anti-spoofing Beyond 2D Attacks construct a computation-efficient and posture-invariant face representation with only a few key points on hypergraphs. The hypergraph representation is then fed into the designed HGCNN with hypergraph convolution for feature extraction, while the depth auxiliary is also exploited for 3D mask anti-spoofing
总结
近几年提出的深度学习的活体检测方法主要有四种思路 1单纯地使用图片作为输入–CNN 难点在于泛化性不足、黑盒特征提出的解决思路有
学习不同数据集的domain differencedomain loss[例5]、FDATPC loss[例10]使用注意力机制学习特定的特征face depthpixel-wise、CNN[例2]新创的抽样方法[例9]新型的网络结构、“ensemble cascade” structure、使用depth image[例8]将spoofing 信息视为一种noise[例3]与人工特征结合[例1]
2考虑时域信息来识别fine-grained motionsLSTM[例7]、rPPG signalsRNN[例2] 3使用无监督学习[例6] 4使用binary image pair[例1]
数据集
可下载 NUAA REPLAY-ATTACK https://www.idiap.ch/dataset/replayattack 、 CASIA-FASD http://www.cbsr.ia.ac.cn/english/FASDB_Agreement/Agreement.pdf SIW http://cvlab.cse.msu.edu/spoof-in-the-wild-siw-face-anti-spoofing-database.html 、 OULU-NPU https://sites.google.com/site/oulunpudatabase/ MSU-MFSD http://biometrics.cse.msu.edu/Publications/Databases/MSUMobileFaceSpoofing/index.htm MSU_USSA http://biometrics.cse.msu.edu/Publications/Databases/MSU_USSA/ HKBU-MARs http://rds.comp.hkbu.edu.hk/mars/ 、 3DMAD https://www.idiap.ch/dataset/3dmad UVAD https://recodbr.wordpress.com/code-n-data/#UVAD REPLAY-MOBILE https://www.idiap.ch/dataset/replay-mobile ROSE-YOUTU http://rose1.ntu.edu.sg/Datasets/faceLivenessDetection.asp CS-MAD https://www.idiap.ch/dataset/csmad SMAD
未公开 SiW-M 、MMFD
参考 活体检测算法综述 论文获取链接1
