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Cross-Database Face Antispoofing with Robust Feature Representation [chapter]

Keyurkumar Patel, Hu Han, Anil K. Jain
2016 Lecture Notes in Computer Science  
We propose a robust representation integrating deep texture features and face movement cue like eye-blink as countermeasures for presentation attacks like photos and replays.  ...  We learn deep texture features from both aligned facial images and whole frames, and use a frame difference based approach for eye-blink detection.  ...  We learn deep texture features from both aligned face images and whole video frames, because texture distortions in spoof face images exist in both face and non-face regions.  ... 
doi:10.1007/978-3-319-46654-5_67 fatcat:iyzbebzqczh7zifj57khozrvi4

Deep Learning for Face Anti-Spoofing: A Survey [article]

Zitong Yu, Yunxiao Qin, Xiaobai Li, Chenxu Zhao, Zhen Lei, Guoying Zhao
2022 arXiv   pre-print
Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs).  ...  As more and more realistic PAs with novel types spring up, traditional FAS methods based on handcrafted features become unreliable due to their limited representation capacity.  ...  Learning discriminative and generalized live/spoof features is vital for deep FAS.  ... 
arXiv:2106.14948v2 fatcat:wsheo7hbwvewhjoe6ykwjuqfii

Deep Frequent Spatial Temporal Learning for Face Anti-Spoofing [article]

Ying Huang, Wenwei Zhang, Jinzhuo Wang
2020 arXiv   pre-print
Face anti-spoofing is crucial for the security of face recognition system, by avoiding invaded with presentation attack.  ...  Compared with existing methods which mine spoofing cues in multi-frame RGB image, we make multi-frame spectrum image as one input stream for the discriminative deep neural network, encouraging the primary  ...  They regard face anti-spoofing as a binary classification problem and learn deep feature representation for single image.  ... 
arXiv:2002.03723v1 fatcat:hhexw3epfjcepcrbt6iaurte4e

Face Anti-Spoofing via Sample Learning Based Recurrent Neural Network (RNN)

Usman Muhammad, Tuomas Holmberg, Wheidima Carneiro de Melo, Abdenour Hadid
2019 British Machine Vision Conference  
We show that for face anti-spoofing task, incorporating sample learning into recurrent structures learn more meaningful representations to LSTM with much fewer model parameters.  ...  The augmented features form as a sequence, which are fed into a Long Short-Term Memory (LSTM) network for constructing the final representation.  ...  With the advent of deep learning, the pretrained CNNs models with linear SVMs (off-theshelf representation) have been proved effective as feature extractors for face anti-spoofing in biometric domain,  ... 
dblp:conf/bmvc/MuhammadHMH19 fatcat:q6tlxtgc6jerrkc4ctuugmkwcm

Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning [article]

Guoqing Wang, Hu Han, Shiguang Shan, Xilin Chen
2020 arXiv   pre-print
In light of this, we propose an efficient disentangled representation learning for cross-domain face PAD.  ...  The disentangled features from different domains are fed to MD-Net which learns domain-independent features for the final cross-domain face PAD task.  ...  MD-Net further learns domain-independent feature representation from the disentangled features for the final cross-domain face PAD task.  ... 
arXiv:2004.01959v1 fatcat:dp6bogljmzainmng6cui4ppla4

Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing [article]

Rizhao Cai, Changsheng Chen
2019 arXiv   pre-print
Face Anti-Spoofing (FAS) is significant for the security of face recognition systems.  ...  Attackers could generate adversarial-spoofing examples to circumvent a CNN-based face liveness detector.  ...  [7] trains a CNN to learn deep representations for face anti-spoofing based on the AlexNet architecture [5] .  ... 
arXiv:1910.03850v1 fatcat:wrlmu2zn35awhibl7mlw4qjwtu

Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning

Guoqing Wang, Hu Han, Shiguang Shan, Xilin Chen
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
In light of this, we propose an efficient disentangled representation learning for cross-domain face PAD.  ...  The disentangled features from different domains are fed to MD-Net which learns domainindependent features for the final cross-domain face PAD task.  ...  spoof faces, such as texture in color space [4, 6] , image distortion [48] , temporal variation [39] or deep semantic features [50, 36] .  ... 
doi:10.1109/cvpr42600.2020.00671 dblp:conf/cvpr/Wang0SC20 fatcat:jqe5sgw2rzcizd3ettvvpulqcq

A Compact Deep Learning Model for Face Spoofing Detection [article]

Seyedkooshan Hashemifard, Mohammad Akbari
2021 arXiv   pre-print
In particular, we simultanously learn a low dimensional latent space empowered with data-driven features learnt via Convolutional Neural Network designes for spoofing detection task (i.e., deep channel  ...  ) as well as leverages spoofing detection feature already popular for spoofing in frequency and temporal dimensions ( i.e., via wide channel).  ...  Deep feature Learning Channel In our experiments, we discovered that very deep neural models are ineffective in learning discriminative features for face spoofing detection task while employing shallower  ... 
arXiv:2101.04756v1 fatcat:2ydd3jentrf5tca7nhmnle65l4

Deep Transfer Across Domains for Face Anti-spoofing [article]

Xiaoguang Tu, Hengsheng Zhang, Mei Xie, Yao Luo, Yuefei Zhang, Zheng Ma
2019 arXiv   pre-print
We propose a CNN framework using sparsely labeled data from the target domain to learn features that are invariant across domains for face anti-spoofing.  ...  Another reason for the poor generalization is that limited labeled data is available for training in face anti-spoofing.  ...  In [5, 7] , the researchers used CNNs to automatically learn features for face anti-spoofing and have achieved promising performance.  ... 
arXiv:1901.05633v1 fatcat:dmoaoxnbqjdllchbp3jafsmq3i

Deep Learning meets Liveness Detection: Recent Advancements and Challenges [article]

Arian Sabaghi, Marzieh Oghbaie, Kooshan Hashemifard, Mohammad Akbari
2021 arXiv   pre-print
Deep feature learning and techniques, as opposed to hand-crafted features, have promised a dramatic increase in the FAS systems' accuracy, tackling the key challenges of materializing the real-world application  ...  Consequently, detecting malicious attempts has found great significance, leading to extensive studies in face anti-spoofing~(FAS),i.e., face presentation attack detection.  ...  “Deep spatial gradient and temporal depth learning for face anti- spoofing”.  ... 
arXiv:2112.14796v1 fatcat:axar6akifnh3lgc4mbuqc5nc2i

El Yapımı Tabanlı ve Derin Öğrenme Yöntemlerini Kullanan Yüz Yanıltma Önleme Şeması

Omid SHARIFI
2020 Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi  
In this context, convolutional neural network (CNN) and Log-Gabor filter methods are used to learn deep representations and extract facial features of images respectively.  ...  The proposed fake detection scheme involves consideration of both handcrafted and deep learned techniques on face images to differentiate real and fake individuals.  ...  The facial images are used to learn deep representation and extract texture information of individuals using convolutional neural network (CNN) [17, 18] and Log-Gabor filter [19] methods respectively  ... 
doi:10.21605/cukurovaummfd.869181 fatcat:pmhoqdeyjnc4bppvtlqjlevchy

A Survey on Face Anti-Spoofing Algorithms

Meigui Zhang, Kehui Zeng, Jinwei Wang
2020 Journal of Information Hiding and Privacy Protection  
based on deep learning.  ...  This paper introduces the research progress of face anti-spoofing algorithm, and divides the existing face anti-spoofing methods into two categories: methods based on manual feature expression and methods  ...  However, the excellence of deep learning in feature extraction still attracted a large number of researchers to engage in face anti-spoofing based on deep learning.  ... 
doi:10.32604/jihpp.2020.010467 fatcat:5otomm6a3jcuxbhyorqq3ofgxi

Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

David Menotti, Giovani Chiachia, Allan Pinto, William Robson Schwartz, Helio Pedrini, Alexandre Xavier Falcao, Anderson Rocha
2015 IEEE Transactions on Information Forensics and Security  
We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches  ...  We consider nine biometric spoofing benchmarks --- each one containing real and fake samples of a given biometric modality and attack type --- and learn deep representations for each benchmark by combining  ...  We envision the application of deep learning representations on top of pre-processed image feature maps (e.g., LBP-like feature maps, acquisition-based maps exploring noise signatures, visual rhythm representations  ... 
doi:10.1109/tifs.2015.2398817 fatcat:zt5sjxdedneufm3sbyeri4fugu

Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues [article]

Zitong Yu, Rizhao Cai, Zhi Li, Wenhan Yang, Jingang Shi, Alex C. Kot
2022 arXiv   pre-print
., remote photoplethysmography (rPPG)) cues; and 2) separated feature representation for FAS or face forgery detection.  ...  FAS and face forgery detection system in a multi-task learning fashion.  ...  We also investigate prevalent deep models, feature fusion strategies and multi-task learning configurations for joint face spoofing and forgery detection.  ... 
arXiv:2208.05401v1 fatcat:5enjp5yt6rfkrnuzinsfpiemmm

Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing

Suman Saha, Wenhao Xu, Menelaos Kanakis, Stamatios Georgoulis, Yuhua Chen, Danda Pani Paudel, Luc Van Gool
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
the learned features.  ...  Face anti-spoofing is a measure towards this direction for biometric user authentication, and in particular face recognition, that tries to prevent spoof attacks.  ...  As can be seen in (b), our model learns more discriminative features for live and spoof images.  ... 
doi:10.1109/cvprw50498.2020.00409 dblp:conf/cvpr/SahaXKGCPG20 fatcat:if6ru5bkjbcbpia6jkixyngehm
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