Academic literature on the topic 'Face spoofing'
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Journal articles on the topic "Face spoofing"
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Qin, Yunxiao, Chenxu Zhao, Xiangyu Zhu, Zezheng Wang, Zitong Yu, Tianyu Fu, Feng Zhou, Jingping Shi, and Zhen Lei. "Learning Meta Model for Zero- and Few-Shot Face Anti-Spoofing." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11916–23. http://dx.doi.org/10.1609/aaai.v34i07.6866.
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Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.
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Su-Gyeong Yu, Su-Gyeong Yu, So-Eui Kim Su-Gyeong Yu, Kun Ha Suh So-Eui Kim, and Eui Chul Lee Kun Ha Suh. "Effect of Facial Shape Information Reflected on Learned Features in Face Spoofing Detection." 網際網路技術學刊 23, no. 3 (May 2022): 517–25. http://dx.doi.org/10.53106/160792642022052303010.
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<p>Face recognition is a convenient and non-contact biometric method used widely for secure personal authentication. However, the face is an exposed body part, and face spoofing attacks, which compromise the security of systems that use face recognition for authentication, are frequently reported. Previous face spoofing attack detection studies proposed texture-analysis-based methods using handcrafted features or learned features to prevent spoofing attacks. However, it is unclear whether spoofing attack images reflect the face distortion resulting from failing to reflect the three-dimensional structure of a real face. To resolve this problem, we compared and analyzed the face spoofing attack detection performances of two typical convolutional neural network models, namely ResNet-18 and DenseNet-121. CASIA-FASD, Replay-Attack, and PR-FSAD were used as the training data. The classification performance of the model was evaluated based on four protocols. DenseNet-121 exhibited better performance in most scenarios. DenseNet-121 reflected facial shape information well by uniformly applying the learned features of both the initial and final layers during training. It is expected that this study will support the realization of spoofing technology with enhanced security.</p> <p> </p>
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Megawan, Sunario, Wulan Sri Lestari, and Apriyanto Halim. "Deteksi Non-Spoofing Wajah pada Video secara Real Time Menggunakan Faster R-CNN." Journal of Information System Research (JOSH) 3, no. 3 (April 29, 2022): 291–99. http://dx.doi.org/10.47065/josh.v3i3.1519.
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Face non-spoofing detection is an important job used to ensure authentication security by performing an analysis of the captured faces. Face spoofing is the process of fake faces by other people to gain illegal access to the biometric system which can be done by displaying videos or images of someone's face on the monitor screen or using printed images. There are various forms of attacks that can be carried out on the face authentication system in the form of face sketches, face photos, face videos and 3D face masks. Such attacks can occur because photos and videos of faces from users of the facial authentication system are very easy to obtain via the internet or cameras. To solve this problem, in this research proposes a non-spoofing face detection model on video using Faster R-CNN. The results obtained in this study are the Faster R-CNN model that can detect non-spoof and spoof face in real time using the Raspberry Pi as a camera with a frame rate of 1 fps.
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Bok, Jin Yeong, Kun Ha Suh, and Eui Chul Lee. "Verifying the Effectiveness of New Face Spoofing DB with Capture Angle and Distance." Electronics 9, no. 4 (April 17, 2020): 661. http://dx.doi.org/10.3390/electronics9040661.
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Face recognition is a representative biometric that can be easily used; however, spoofing attacks threaten the security of face biometric systems by generating fake faces. Thus, it is not advisable to only consider sophisticated spoofing cases, such as three-dimensional masks, because they require additional equipment, thereby increasing the implementation cost. To prevent easy face spoofing attacks through print and display, the two-dimensional (2D) image analysis method using existing face recognition systems is reasonable. Therefore, we proposed a new database called the “pattern recognition-face spoofing advancement database” that can be used to prevent such attacks based on 2D image analysis. To the best of our knowledge, this is the first face spoofing database that considers the changes in both the angle and distance. Therefore, it can be used to train various positional relationships between a face and camera. We conducted various experiments to verify the efficiency of this database. The spoofing detection accuracy of our database using ResNet-18 was found to be 96.75%. The experimental results for various scenarios demonstrated that the spoof detection performances were better for images with pinch angle, near distance images, and replay attacks than those for front images, far distance images, and print attacks, respectively. In the cross-database verification result, the performance when tested with other databases (DBs) after training with our DB was better than the opposite. The results of cross-device verification in terms of camera type showed negligible difference; thus, it was concluded that the type of image sensor does not affect the detection accuracy. Consequently, it was confirmed that the proposed DB that considers various distances, capture angles, lighting conditions, and backgrounds can be used as a training DB to detect spoofing attacks in general face recognition systems.
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Abusham, Eimad, Basil Ibrahim, Kashif Zia, and Muhammad Rehman. "Facial Image Encryption for Secure Face Recognition System." Electronics 12, no. 3 (February 3, 2023): 774. http://dx.doi.org/10.3390/electronics12030774.
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A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. This paper proposes an image encryption scheme to counter spoofing attacks by integrating it into the pipeline of Linear Discriminant Analysis (LDA) based face recognition. The encryption scheme uses XOR pixels substitution and cellular automata for scrambling. A single key is used to encrypt the training and testing datasets in LDA face recognition system. For added security, the encryption step requires input images of faces to be encrypted with the correct key before the system can recognize the images. An LDA face recognition scheme based on random forest classifiers has achieved 96.25% accuracy on ORL dataset in classifying encrypted test face images. In a test where original test face images were not encrypted with keys used for encrypted feature databases, the system achieved 8.75% accuracy only showing it is capable of resisting spoofing attacks.
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Perdana, Rizky Naufal, Igi Ardiyanto, and Hanung Adi Nugroho. "A Review on Face Anti-Spoofing." IJITEE (International Journal of Information Technology and Electrical Engineering) 5, no. 1 (June 18, 2021): 29. http://dx.doi.org/10.22146/ijitee.61827.
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The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types.
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Kim, Seung-Hyun, Su-Min Jeon, and Eui Chul Lee. "Face Biometric Spoof Detection Method Using a Remote Photoplethysmography Signal." Sensors 22, no. 8 (April 16, 2022): 3070. http://dx.doi.org/10.3390/s22083070.
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Spoofing attacks in face recognition systems are easy because faces are always exposed. Various remote photoplethysmography-based methods to detect face spoofing have been developed. However, they are vulnerable to replay attacks. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that minimizes the susceptibility to certain database dependencies and high-quality replay attacks without additional devices. The proposed method has the following advantages. First, because only an RGB camera is used to detect spoofing attacks, the proposed method is highly usable in various mobile environments. Second, solutions are incorporated in the method to obviate new attack scenarios that have not been previously dealt with. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that improves susceptibility to certain database dependencies and high-quality replay attack, which are the limitations of previous methods without additional devices. In the experiment, we also verified the cut-off attack scenario in the jaw and cheek area where the proposed method can be counter-attacked. By using the time series feature and the frequency feature of the remote photoplethysmography signal, it was confirmed that the accuracy of spoof detection was 99.7424%.
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H, Vinutha, and Thippeswamy G. "Antispoofing in face biometrics: a comprehensive study on software-based techniques." Computer Science and Information Technologies 4, no. 1 (March 1, 2023): 1–13. http://dx.doi.org/10.11591/csit.v4i1.p1-13.
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The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop anti-spoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques.
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Zahra, Sayyam, Mohibullah Khan, Kamran Abid, Naeem Aslam, and Ejaz Ahmad Khera. "A Novel Face Spoofing Detection Using hand crafted MobileNet." VFAST Transactions on Software Engineering 11, no. 2 (June 2, 2023): 34–42. http://dx.doi.org/10.21015/vtse.v11i2.1485.
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There are several uses for face spoofing detection, including human-robot communication, business, film, hotel services, and even politics. Despite the adoption of numerous supervised and unsupervised techniques in a wide range of domains, proper analysis is still lacking. As a result, we chose this difficulty as our study problem. We have put out a method for the effective and precise classification of face spoofing that may be used for a variety of everyday issues. This work attempts to investigate the ideal method and parameters to offer a solution for a powerful deep learning spoofing detection system. In this study, we used the LCC FASD dataset and deep learning algorithms to recognize faces from photos. Precision and accuracy are used as the evaluation measures to assess the performance of the CNN (Convolutional Neural Network) model. The results of the studies demonstrate that the model was effective at spoofing face picture detection. The accuracy of the CNN model was 0.98. Overall, the study's findings show that spoofing detection from photos using the LCC FASD dataset can be successfully performed utilizing deep learning algorithms. Yet, the findings of this study offer a strong framework for further investigation in this area.
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Dave, Vani. "Spoof Detection Using Local Binary Pattern In Face." Jurnal Ilmu Komputer 13, no. 1 (April 29, 2020): 39. http://dx.doi.org/10.24843/jik.2020.v13.i01.p05.
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Spoofing attack is an attempt to acquire some other’s identity or access right by using a biometric evidence of authorized user. Among all biometric systems facial identity is one of the widely used method that is prone to such spoofing attacks using a simple photograph of the user.
The paper focuses and takes the problem area of face spoofing attacks into account by detecting spoof faces and real faces. We are using the local binary pattern (LBP) for providing the solution of spoofing problem and with the help of these patterns we inspect primarily two types of attacks i.e. printed photograph and photos displayed using digital screen. For this, we will use the local database maintained by us having the images labeled as real and spoof for the data required.
We conclude that local binary pattern will reduce the total error rate and will show the moderate output when used across a wide set of attack types. This will enhance the efficiency of the system for detection of spoofing by using the deep learning techniques
Dissertations / Theses on the topic "Face spoofing"
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Abd, Aziz Azim Zaliha Binti. "Vision-based spoofing face detection using polarised light." Thesis, University of Reading, 2017. http://centaur.reading.ac.uk/75434/.
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Computer vision is an image understanding discipline that studies how to reconstruct, interpret and understand a 3D scene from its 2D images. One of the goals is to automate the analysis of images through the use of computer software and hardware. Meanwhile, biometrics refer to the automated authentication process that rely on measureable physical characteristics such as individual’s unique fingerprints, iris, face, palmprint, gait and voice. Amongst these biometric identification schemes, face biometric is said to be the most popular where face authentication systems have been rapidly developed mainly for security reasons. However, the resistance of face biometric system to spoofing attack, which is an act to impersonate a valid user by placing fake face in front of the sensor to gain access, has become a critical issue. Thus, anti-spoofing technique is required to counter the attacks. Different materials have their own reflection properties. These reflection differences have been manipulated by researches for particular reasons such as in object classification. Many ways can be used to measure the reflection differences of each object. One of them is by using polarised light. Since none of the existing studies applied polarised light in face spoofing detection, therefore in this thesis, polarisation imaging technique was implemented to distinguish between genuine face and two types of spoofing attacks: printed photos and iPad displayed faces. From the investigations, several research findings can be listed. Firstly, unpolarised visible light could not be used in a polarisation imaging system to capture polarised images for designated purpose. Secondly, polarised light is able to differentiate between surface and subsurface reflections of real and fake faces. However, both of these reflections could not be used as one of the classification methods between real face and printed photos. Thirdly, polarised image could contribute to enhance the performance of face recognition system against spoofing attacks in which the newly proposed formula, SDOLP3F achieves higher accuracy rate. Next, near infrared (NIR) light in a polarisation imaging system do not provide significant differences between real face and the two face attacks. Apart from polarised spoofing face detection analysis, experiments to investigate the accuracy of depth data captured by three depth sensors was carried out. This investigation was conducted due to the concerns over the stability of the depth pixels involved in 3D spoofing face reconstruction in a publicly available spoofing face database known as 3DMAD. From the analysis, none of the three depth sensors which are the Kinect for Xbox 360, Kinect for Windows version 2.0 and Asus Xtion Pro Live are suitable for 3D face reconstruction for the purpose of spoofing detection due to the potential errors made by the fluctuated pixels. As a conclusion, polarisation imaging technique has the potential to protect face biometric system from printed photos and iPad displayed attacks. Further investigations using the same polarised light approach could be carried out on other future work as proposed at the end of this thesis.
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Edmunds, Taiamiti. "Protection of 2D face identification systems against spoofing attacks." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAT007/document.
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Les systèmes d’identification faciale sont en plein essor et se retrouvent de plus en plus dans des produits grand public tels que les smartphones et les ordinateurs portables. Cependant, ces systèmes peuvent être facilement bernés par la présentation par exemple d’une photo imprimée de la personne ayant les droits d’accès au système. Cette thèse s’inscrit dans le cadre du projet ANR BIOFENCE qui vise à développer une certification des systèmes biométriques veine, iris et visage permettant aux industriels de faire valoir leurs innovations en termes de protection. L’objectif de cette thèse est double, d’abord il s’agit de développer des mesures de protection des systèmes 2D d’identification faciale vis à vis des attaques connues à ce jour (photos imprimées, photos ou vidéos sur un écran, masques) puis de les confronter à la méthodologie de certification développée au sein du projet ANR. Dans un premier temps, un état de l’art général des attaques et des contremesures est présenté en mettant en avant les méthodes algorithmiques (« software ») par rapport aux méthodes hardware. Ensuite, plusieurs axes sont approfondis au cours de ce travail. Le premier concerne le développement d’une contremesure basée sur une analyse de texture et le second concerne le développement d’une contre-mesure basée sur une analyse de mouvement. Ensuite, une modélisation du processus de recapture pour différencier un faux visage d’un vrai est proposée. Une nouvelle méthode de protection est développée sur ce concept en utilisant les données d'enrolment des utilisateurs et un premier pas est franchi dans la synthèse d'attaque pour un nouvel utilisateur à partir de sa donnée d'enrolment. Enfin, la méthodologie de certification développée pour les systèmes à empreintes digitales est évaluée pour les systèmes d'identification facialFace identification systems are growing rapidly and invade the consumer market with security products in smartphones, computers and banking. However, these systems are easily fooled by presenting a picture of the person having legitimate access to the system. This thesis is part of the BIOFENCE project which aim to develop a certification of biometric systems in order for industrials to promote their innovations in terms of protection. Our goal is to develop new anti-spoofing countermeasures for 2D face biometric systems and to evaluate the certification methodology on protected systems. First, a general state of the art in face spoofing attack forgery and in anti-spoofing protection measures is presented. Then texture-based countermeasures and motion-based countermeasures are investigated leading to the development of two novel countermeasures. Then, the recapturing process is modelled and a new fake face detection approach is proposed based on this model. Taking advantage of enrolment samples from valid users, a first step toward the synthesis of spoofing attacks for new users is taken. Finally, the certification methodology originally developed for fingerprint technology is evaluated on face biometric systems
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Pereira, Tiago de Freitas 1985. "A comparative study of countermeasures to detect spoofing attacks in face authentication systems = Um estudo comparativo de contramedidas para detectar ataques de spoofing em sistemas de autenticação de faces." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261478.
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Orientador: José Mario De MartinoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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Resumo: O Resumo poderá ser visualizado no texto completo da tese digital
Abstract: The complete Abstract is available with the full electronic document.
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica
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Komulainen, J. (Jukka). "Software-based countermeasures to 2D facial spoofing attacks." Doctoral thesis, Oulun yliopisto, 2015. http://urn.fi/urn:isbn:9789526208732.
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Abstract Because of its natural and non-intrusive interaction, identity verification and recognition using facial information is among the most active areas in computer vision research. Unfortunately, it has been shown that conventional 2D face recognition techniques are vulnerable to spoofing attacks, where a person tries to masquerade as another one by falsifying biometric data and thereby gaining an illegitimate advantage. This thesis explores different directions for software-based face anti-spoofing. The proposed approaches are divided into two categories: first, low-level feature descriptors are applied for describing the static and dynamic characteristic differences between genuine faces and fake ones in general, and second, complementary attack-specific countermeasures are investigated in order to overcome the limitations of generic spoof detection schemes. The static face representation is based on a set of well-known feature descriptors, including local binary patterns, Gabor wavelet features and histogram of oriented gradients. The key idea is to capture the differences in quality, light reflection and shading by analysing the texture and gradient structure of the input face images. The approach is then extended to the spatiotemporal domain when both facial appearance and dynamics are exploited for spoof detection using local binary patterns from three orthogonal planes. It is reasonable to assume that no generic spoof detection scheme is able to detect all known, let alone unseen, attacks scenarios. In order to find out well-generalizing countermeasures, the problem of anti-spoofing is broken into two attack-specific sub-problems based on whether the spoofing medium can be detected in the provided view or not. The spoofing medium detection is performed by describing the discontinuities in the gradient structures around the detected face. If the display medium is concealed outside the view, a combination of face and background motion correlation measurement and texture analysis is applied. Furthermore, an open-source anti-spoofing fusion framework is introduced and its system-level performance is investigated more closely in order to gain insight on how to combine different anti-spoofing modules. The proposed spoof detection schemes are evaluated on the latest benchmark datasets. The main findings of the experiments are discussed in the thesisTiivistelmä Kasvokuvaan perustuvan henkilöllisyyden tunnistamisen etuja ovat luonnollinen vuorovaikutus ja etätunnistus, minkä takia aihe on ollut erittäin aktiivinen tutkimusalue konenäön tutkimuksessa. Valitettavasti tavanomaiset kasvontunnistustekniikat ovat osoittautuneet haavoittuvaisiksi hyökkäyksille, joissa kameralle esitetään jäljennös kohdehenkilön kasvoista positiivisen tunnistuksen toivossa. Tässä väitöskirjassa tutkitaan erilaisia ohjelmistopohjaisia ratkaisuja keinotekoisten kasvojen ilmaisuun petkuttamisen estämiseksi. Työn ensimmäisessä osassa käytetään erilaisia matalan tason piirteitä kuvaamaan aitojen ja keinotekoisten kasvojen luontaisia staattisia ja dynaamisia eroavaisuuksia. Työn toisessa osassa esitetään toisiaan täydentäviä hyökkäystyyppikohtaisia vastakeinoja, jotta yleispätevien menetelmien puutteet voitaisiin ratkaista ongelmaa rajaamalla. Kasvojen staattisten ominaisuuksien esitys perustuu yleisesti tunnettuihin matalan tason piirteisiin, kuten paikallisiin binäärikuvioihin, Gabor-tekstuureihin ja suunnattujen gradienttien histogrammeihin. Pääajatuksena on kuvata aitojen ja keinotekoisten kasvojen laadun, heijastumisen ja varjostumisen eroavaisuuksia tekstuuria ja gradienttirakenteita analysoimalla. Lähestymistapaa laajennetaan myös tila-aika-avaruuteen, jolloin hyödynnetään samanaikaisesti sekä kasvojen ulkonäköä ja dynamiikkaa irroittamalla paikallisia binäärikuvioita tila-aika-avaruuden kolmelta ortogonaaliselta tasolta. Voidaan olettaa, ettei ole olemassa yksittäistä yleispätevää vastakeinoa, joka kykenee ilmaisemaan jokaisen tunnetun hyökkäystyypin, saati tuntemattoman. Näin ollen työssä keskitytään tarkemmin kahteen hyökkäystilanteeseen. Ensimmäisessä tapauksessa huijausapuvälineen reunoja ilmaistaan analysoimalla gradienttirakenteiden epäjatkuvuuksia havaittujen kasvojen ympäristössä. Jos apuvälineen reunat on piilotettu kameran näkymän ulkopuolelle, petkuttamisen ilmaisu toteutetaan yhdistämällä kasvojen ja taustan liikkeen korrelaation mittausta ja kasvojen tekstuurianalyysiä. Lisäksi työssä esitellään vastakeinojen yhdistämiseen avoimen lähdekoodin ohjelmisto, jonka avulla tutkitaan lähemmin menetelmien fuusion vaikutuksia. Tutkimuksessa esitetyt menetelmät on kokeellisesti vahvistettu alan viimeisimmillä julkisesti saatavilla olevilla tietokannoilla. Tässä väitöskirjassa käydään läpi kokeiden päähavainnot
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Pinto, Allan da Silva 1984. "A countermeasure method for video-based face spoofing attacks : Detecção de tentativas de ataque com vídeos digitais em sistemas de biometria de face." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275616.
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Orientador: Anderson de Rezende RochaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: O resumo poderá ser visualizado no texto completo da tese digital
Abstract: The complete abstract is available with the full electronic document
Mestrado
Ciência da Computação
Mestre em Ciência da Computação
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Boulkenafet, Z. (Zinelabidine). "Face presentation attack detection using texture analysis." Doctoral thesis, Oulun yliopisto, 2018. http://urn.fi/urn:isbn:9789526219257.
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Abstract In the last decades, face recognition systems have evolved a lot in terms of performance. As a result, this technology is now considered as mature and is applied in many real world applications from border control to financial transactions and computer security. Yet, many studies show that these systems suffer from vulnerabilities to spoofing attacks, a weakness that may limit their usage in many cases. A face spoofing attack or presentation attack occurs when someone tries to masquerade as someone else by presenting a fake face in front of the face recognition camera. To protect the recognition systems against attacks of this kind, many face anti-spoofing methods have been proposed. These methods have shown good performances on the existing face anti-spoofing databases. However, their performances degrade drastically under real world variations (e.g., illumination and camera device variations). In this thesis, we concentrate on improving the generalization capabilities of the face anti-spoofing methods with a particular focus on the texture based techniques. In contrast to most existing texture based methods aiming at extracting texture features from gray-scale images, we propose a joint color-texture analysis. First, the face images are converted into different color spaces. Then, the feature histograms computed over each image band are concatenated and used for discriminating between real and fake face images. Our experiments conducted on three color spaces: RGB, HSV and YCbCr show that extracting the texture information from separated luminance chrominance color spaces (HSV and YCbCr) yields to better performances compared to gray-scale and RGB image representations. Moreover, to deal with the problem of illumination and image-resolution variations, we propose to extract this texture information from different scale images. In addition to representing the face images in different scales, the multi-scale filtering methods also act as pre-processing against factors such as noise and illumination. Although our obtained results are better than the state of the art, they are still far from the requirements of real world applications. Thus, to help in the development of robust face anti-spoofing methods, we collected a new challenging face anti-spoofing database using six camera devices in three different illumination and environmental conditions. Furthermore, we have organized a competition on the collected database where fourteen face anti-spoofing methods have been assessed and comparedTiivistelmä Kasvontunnistusjärjestelmien suorituskyky on parantunut huomattavasti viime vuosina. Tästä syystä tätä teknologiaa pidetään nykyisin riittävän kypsänä ja käytetään jo useissa käytännön sovelluksissa kuten rajatarkastuksissa, rahansiirroissa ja tietoturvasovelluksissa. Monissa tutkimuksissa on kuitenkin havaittu, että nämä järjestelmät ovat myös haavoittuvia huijausyrityksille, joissa joku yrittää esiintyä jonakin toisena henkilönä esittämällä kameralle jäljennöksen kohdehenkilön kasvoista. Tämä haavoittuvuus rajoittaa kasvontunnistuksen laajempaa käyttöä monissa sovelluksissa. Tunnistusjärjestelmien turvaamiseksi on kehitetty lukuisia menetelmiä tällaisten hyökkäysten torjumiseksi. Nämä menetelmät ovat toimineet hyvin tätä tarkoitusta varten kehitetyillä kasvotietokannoilla, mutta niiden suorituskyky huononee dramaattisesti todellisissa käytännön olosuhteissa, esim. valaistuksen ja käytetyn kuvantamistekniikan variaatioista johtuen. Tässä työssä yritämme parantaa kasvontunnistuksen huijauksen estomenetelmien yleistämiskykyä keskittyen erityisesti tekstuuripohjaisiin menetelmiin. Toisin kuin useimmat olemassa olevat tekstuuripohjaiset menetelmät, joissa tekstuuripiirteitä irrotetaan harmaasävykuvista, ehdotamme väritekstuurianalyysiin pohjautuvaa ratkaisua. Ensin kasvokuvat muutetaan erilaisiin väriavaruuksiin. Sen jälkeen kuvan jokaiselta kanavalta erikseen lasketut piirrehistogrammit yhdistetään ja käytetään erottamaan aidot ja väärät kasvokuvat toisistaan. Kolmeen eri väriavaruuteen, RGB, HSV ja YCbCr, perustuvat testimme osoittavat, että tekstuuri-informaation irrottaminen HSV- ja YCbCr-väriavaruuksien erillisistä luminanssi- ja krominanssikuvista parantaa suorituskykyä kuvien harmaasävy- ja RGB-esitystapoihin verrattuna. Valaistuksen ja kuvaresoluution variaation takia ehdotamme myös tämän tekstuuri-informaation irrottamista eri tavoin skaalatuista kuvista. Sen lisäksi, että itse kasvot esitetään eri skaaloissa, useaan skaalaan perustuvat suodatusmenetelmät toimivat myös esikäsittelynä sellaisia suorituskykyä heikentäviä tekijöitä vastaan kuten kohina ja valaistus. Vaikka tässä tutkimuksessa saavutetut tulokset ovat parempia kuin uusinta tekniikkaa edustavat tulokset, ne ovat kuitenkin vielä riittämättömiä reaalimaailman sovelluksissa tarvittavaan suorituskykyyn. Sen takia edistääksemme uusien robustien kasvontunnistuksen huijaamisen ilmaisumenetelmien kehittämistä kokosimme uuden, haasteellisen huijauksenestotietokannan käyttäen kuutta kameraa kolmessa erilaisessa valaistus- ja ympäristöolosuhteessa. Järjestimme keräämällämme tietokannalla myös kansainvälisen kilpailun, jossa arvioitiin ja verrattiin neljäätoista kasvontunnistuksen huijaamisen ilmaisumenetelmää
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Tang, Yinhang. "Contributions to biometrics : curvatures, heterogeneous cross-resolution FR and anti spoofing." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEC060/document.
Full textAbstract:
Visage est l’une des meilleures biométries pour la reconnaissance de l’identité de personnes, car l’identification d’une personne par le visage est l’habitude instinctive humaine, et l’acquisition de données faciales est naturelle, non intrusive et bien acceptée par le public. Contrairement à la reconnaissance de visage par l’image 2D sur l’apparence, la reconnaissance de visage en 3D sur la forme est théoriquement plus stable et plus robuste à la variance d’éclairage, aux petits changements de pose de la tête et aux cosmétiques pour le visage. Spécifiquement, les courbures sont les plus importants attributs géométriques pour décrire la forme géométrique d’une surface. Elles sont bénéfiques à la caractérisation de la forme du visage qui permet de diminuer l’impact des variances environnementales. Cependant, les courbures traditionnelles ne sont définies que sur des surfaces lisses. Il est donc nécessaire de généraliser telles notions sur des surfaces discrètes, par exemple des visages 3D représenté par maillage triangulaire, et d’évaluer leurs performances en reconnaissance de visage 3D. En outre, même si un certain nombre d’algorithmes 3D FR avec une grande précision sont disponibles, le coût d’acquisition de telles données de haute résolution est difficilement acceptable pour les applications pratiques. Une question majeure est donc d’exploiter les algorithmes existants pour la reconnaissance de modèles à faible résolution collecté avec l’aide d’un nombre croissant de caméras consommateur de profondeur (Kinect). Le dernier problème, mais non le moindre, est la menace sur sécurité des systèmes de reconnaissance de visage 3D par les attaques de masque fabriqué. Cette thèse est consacrée à l’étude des attributs géométriques, des mesures de courbure principale, adaptées aux maillages triangulaires, et des schémas de reconnaissance de visage 3D impliquant des telles mesures de courbure principale. En plus, nous proposons aussi un schéma de vérification sur la reconnaissance de visage 3D collecté en comparant des modèles de résolutions hétérogènes équipement aux deux résolutions, et nous évaluons la performance anti-spoofing du système de RF 3D. Finalement, nous proposons une biométrie système complémentaire de reconnaissance veineuse de main basé sur la détection de vivacité et évaluons sa performance. Dans la reconnaissance de visage 3D par la forme géométrique, nous introduisons la généralisation des courbures principales conventionnelles et des directions principales aux cas des surfaces discrètes à maillage triangulaire, et présentons les concepts des mesures de courbure principale correspondants et des vecteurs de courbure principale. Utilisant ces courbures généralisées, nous élaborons deux descriptions de visage 3D et deux schémas de reconnaissance correspondent. Avec le premier descripteur de caractéristiques, appelé Local Principal Curvature Measures Pattern (LPCMP), nous générons trois images spéciales, appelée curvature faces, correspondant à trois mesures de courbure principale et encodons les curvature faces suivant la méthode de Local Binary Pattern. Il peut décrire la surface faciale de façon exhaustive par l’information de forme locale en concaténant un ensemble d’histogrammes calculés à partir de petits patchs dans les visages de courbure. Dans le deuxième système de reconnaissance de visage 3D sans enregistrement, appelée Principal Curvature Measures based meshSIFT descriptor (PCM-meshSIFT), les mesures de courbure principales sont d’abord calculées dans l’espace de l’échelle Gaussienne, et les extrèmes de la Différence de Courbure (DoC) sont définis comme les points de caractéristique. Ensuite, nous utilisons trois mesures de courbure principales et leurs vecteurs de courbure principaux correspondants pour construire trois descripteurs locaux pour chaque point caractéristique, qui sont invariants en rotation. [...]Face is one of the best biometrics for person recognition related application, because identifying a person by face is human instinctive habit, and facial data acquisition is natural, non-intrusive, and socially well accepted. In contrast to traditional appearance-based 2D face recognition, shape-based 3D face recognition is theoretically more stable and robust to illumination variance, small head pose changes, and facial cosmetics. The curvatures are the most important geometric attributes to describe the shape of a smooth surface. They are beneficial to facial shape characterization which makes it possible to decrease the impact of environmental variances. However, exiting curvature measurements are only defined on smooth surface. It is required to generalize such notions to discrete meshed surface, e.g., 3D face scans, and to evaluate their performance in 3D face recognition. Furthermore, even though a number of 3D FR algorithms with high accuracy are available, they all require high-resolution 3D scans whose acquisition cost is too expensive to prevent them to be implemented in real-life applications. A major question is thus how to leverage the existing 3D FR algorithms and low-resolution 3D face scans which are readily available using an increasing number of depth-consumer cameras, e.g., Kinect. The last but not least problem is the security threat from spoofing attacks on 3D face recognition system. This thesis is dedicated to study the geometric attributes, principal curvature measures, suitable to triangle meshes, and the 3D face recognition schemes involving principal curvature measures. Meanwhile, based on these approaches, we propose a heterogeneous cross-resolution 3D FR scheme, evaluate the anti-spoofing performance of shape-analysis based 3D face recognition system, and design a supplementary hand-dorsa vein recognition system based on liveness detection with discriminative power. In 3D shape-based face recognition, we introduce the generalization of the conventional point-wise principal curvatures and principal directions for fitting triangle mesh case, and present the concepts of principal curvature measures and principal curvature vectors. Based on these generalized curvatures, we design two 3D face descriptions and recognition frameworks. With the first feature description, named as Local Principal Curvature Measures Pattern descriptor (LPCMP), we generate three curvature faces corresponding to three principal curvature measures, and encode the curvature faces following Local Binary Pattern method. It can comprehensively describe the local shape information of 3D facial surface by concatenating a set of histograms calculated from small patches in the encoded curvature faces. In the second registration-free feature description, named as Principal Curvature Measures based meshSIFT descriptor (PCM-meshSIFT), the principal curvature measures are firstly computed in the Gaussian scale space, and the extremum of Difference of Curvautre (DoC) is defined as keypoints. Then we employ three principal curvature measures and their corresponding principal curvature vectors to build three rotation-invariant local 3D shape descriptors for each keypoint, and adopt the sparse representation-based classifier for keypoint matching. The comprehensive experimental results based on FRGCv2 database and Bosphorus database demonstrate that our proposed 3D face recognition scheme are effective for face recognition and robust to poses and occlusions variations. Besides, the combination of the complementary shape-based information described by three principal curvature measures significantly improves the recognition ability of system. To deal with the problem towards heterogeneous cross-resolution 3D FR, we continuous to adopt the PCM-meshSIFT based feature descriptor to perform the related 3D face recognition. [...]
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Li, X. (Xiaobai). "Reading subtle information from human faces." Doctoral thesis, Oulun yliopisto, 2017. http://urn.fi/urn:isbn:9789526216386.
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Abstract The face plays an important role in our social interactions as it conveys rich sources of information. We can read a lot from one face image, but there is also information we cannot perceive without special devices. The thesis concerns using computer vision methodologies to analyse two kinds of subtle facial information that can hardly be perceived by naked eyes: the micro-expression (ME), and the heart rate (HR). MEs are rapid, involuntary facial expressions which reveal emotions people do not intend to show. It is difficult for people to perceive MEs as they are too fast and subtle, thus automatic ME analysis is valuable work which may lead to important applications. In the thesis, the progresses of ME studies are reviewed, and four parts of work are described. 1) We introduce the first spontaneous ME database, the SMIC. The lacking of data is hindering ME analysis research, as it is difficult to collect spontaneous MEs. The protocol for inducing and annotating SMIC is introduced to help future ME collections. 2) A framework including three features and a video magnification process is introduced for ME recognition, which outperforms other state-of-the-art methods on two ME databases. 3) An ME spotting method based on feature difference analysis is described, which can spot MEs from spontaneous long videos. 4) An automatic ME analysis system (MESR) was proposed for firstly spotting and then recognising MEs. The HR is an important indicator of our health and emotional status. Traditional HR measurements require skin-contact which cannot be applied remotely. We propose a method which can counter for illumination changes and head motions and measure HR remotely from color facial videos. We also apply the method for solving the face anti-spoofing problem. We show that the pulse-based feature is more robust than traditional texture-based features against unseen mask spoofs. We also show that the proposed pulse-based feature can be combined with other features to build a cascade system for detecting multiple types of attacks. At last, we summarize the contributions of the work, and propose future plans about ME and HR studies based on limitations of the current work. It is also planned to combine the ME and HR (maybe also other subtle signals from face) to build a multimodal system for affective status analysisTiivistelmä Kasvot ovat monipuolinen informaatiolähde ja keskeinen ihmisten välisessä vuorovaikutuksessa. Pystymme päättelemään paljon yhdestäkin kasvokuvasta, mutta kasvoissa on paljon tietoa, jota ei pysty irrottamaan ilman erityiskeinoja. Tässä työssä analysoidaan konenäöllä ihmiselle vaikeasti havaittavaa tietoa: mikroilmeitä ja sydämen sykettä. Tahdosta riippumattomat mikroilmeet paljastavat tunteita, joita ihmiset pyrkivät piilottamaan. Mikroilmeiden havaitseminen on vaikeaa niiden nopeuden ja pienuuden vuoksi, joten automaattinen analyysi voi johtaa uusiin merkittäviin sovelluksiin. Tämä työ tarkastelee mikroilmetutkimuksen edistysaskeleita ja sisältää neljä uutta tulosta. 1) Spontaanien mikroilmeiden tietokanta (Spontaneous MIcroexpression Corpus, SMIC). Spontaanien mikroilmeiden aiheuttaminen datan saamiseksi on oma haasteensa. SMIC:n keräämisessä ja mikroilmeiden annotoinnissa käytetty menettely on kuvattu myöhemmän datan keruun ohjeistukseksi. 2) Aiempia mikroilmeiden tunnistusmenetelmiä paremmaksi kahden testitietokannan avulla todennettu ratkaisu, joka käyttää kolmea eri piirrettä ja videon suurennusta. 3) Piirre-eroanalyysiin perustuva mikroilmeiden havaitsemismenetelmä, joka havaitsee ne pitkistä realistisista videoista. 4) Automaattinen analyysijärjestelmä (Micro-Expression Spotting and Recognition, MESR), jossa mikroilmeet havaitaan ja tunnistetaan. Sydämen syke on tärkeä terveyden ja tunteiden indikoija. Perinteiset sykkeenmittausmenetelmät vaativat ihokontaktia, eivätkä siten toimii etäältä. Tässä työssä esitetään sykkeen videolta pienistä värimuutoksista mittaava menetelmä, joka sietää valaistusmuutoksia ja sallii pään liikkeet. Menetelmä on monikäyttöinen ja sen sovelluksena kuvataan todellisten kasvojen varmentaminen sykemittauksella. Tulokset osoittavat sykepiirteiden toimivan perinteisiä tekstuuripiirteitä paremmin uudenlaisia naamarihuijauksia vastaan. Syketietoa voidaan myös käyttää osana sarjatyyppisissä ratkaisuissa havaitsemaan useanlaisia huijausyrityksiä. Työn yhteenveto keskittyy suunnitelmiin parantaa mikroilmeiden ja sydämen sykkeen analyysimenetelmiä nykyisen tutkimuksen rajoitteiden pohjalta. Tavoitteena on yhdistää mikroilmeiden ja sydämen sykkeen analyysit, sekä mahdollisesti muuta kasvoista saatavaa tietoa, multimodaaliseksi affektiivisen tilan määrittäväksi ratkaisuksi
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Sarkar, Abhijit. "Cardiac Signals: Remote Measurement and Applications." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78739.
Full textAbstract:
The dissertation investigates the promises and challenges for application of cardiac signals in biometrics and affective computing, and noninvasive measurement of cardiac signals. We have mainly discussed two major cardiac signals: electrocardiogram (ECG), and photoplethysmogram (PPG).
ECG and PPG signals hold strong potential for biometric authentications and identifications. We have shown that by mapping each cardiac beat from time domain to an angular domain using a limit cycle, intra-class variability can be significantly minimized. This is in contrary to conventional time domain analysis. Our experiments with both ECG and PPG signal shows that the proposed method eliminates the effect of instantaneous heart rate on the shape morphology and improves authentication accuracy. For noninvasive measurement of PPG beats, we have developed a systematic algorithm to extract pulse rate from face video in diverse situations using video magnification. We have extracted signals from skin patches and then used frequency domain correlation to filter out non-cardiac signals. We have developed a novel entropy based method to automatically select skin patches from face. We report beat-to-beat accuracy of remote PPG (rPPG) in comparison to conventional average heart rate. The beat-to-beat accuracy is required for applications related to heart rate variability (HRV) and affective computing. The algorithm has been tested on two datasets, one with static illumination condition and the other with unrestricted ambient illumination condition.
Automatic skin detection is an intermediate step for rPPG. Existing methods always depend on color information to detect human skin. We have developed a novel standalone skin detection method to show that it is not necessary to have color cues for skin detection. We have used LBP lacunarity based micro-textures features and a region growing algorithm to find skin pixels in an image. Our experiment shows that the proposed method is applicable universally to any image including near infra-red images. This finding helps to extend the domain of many application including rPPG. To the best of our knowledge, this is first such method that is independent of color cues.Ph. D.
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Trabelsi, Anis. "Robustesse aux attaques en authentification digitale par apprentissage profond." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS580.
Full textAbstract:
L'identité des personnes sur Internet devient un problème de sécurité majeur. Depuis les accords de Bale, les institutions bancaires ont intégré la vérification de l'identité des personnes ou Know Your Customer (KYC) dans leur processus d'inscription. Avec la dématérialisation des banques, cette procédure est devenue l'e-KYC ou KYC à distance qui fonctionne à distance via le smartphone de l'utilisateur. De même, la vérification d'identité à distance est devenue la norme pour l'inscription aux outils de signature électronique. De nouvelles réglementations émergent pour sécuriser cette approche, par exemple, en France, le cadre PVID encadre l'acquisition à distance des documents d'identité et du visage des personnes dans le cadre du règlement eIDAS. Cela est nécessaire, car on assiste à l'émergence d'un nouveau type de criminalité numérique : l'usurpation d'identité profonde. Grâce aux nouveaux outils d'apprentissage profond, les imposteurs peuvent modifier leur apparence pour ressembler à quelqu'un d'autre en temps réel. Les imposteurs peuvent alors accomplir toutes les actions courantes requises lors d'une inscription à distance sans être détectés par les algorithmes de vérification d'identité. Aujourd'hui, il existe des applications sur smartphone et des outils destinés à un public plus limité qui permettent aux imposteurs de transformer facilement leur apparence en temps réel. Il existe même des méthodes pour usurper une identité à partir d'une seule image du visage de la victime. L'objectif de cette thèse est d'étudier les vulnérabilités des systèmes d'authentification d'identité à distance face aux nouvelles attaquesThe identity of people on the Internet is becoming a major security issue. Since the Bale agreements, banking institutions have integrated the verification of people's identity or Know Your Customer (KYC) in their registration process. With the dematerialization of banks, this procedure has become e-KYC or remote KYC which works remotely through the user's smartphone. Similarly, remote identity verification has become the standard for enrollment in electronic signature tools. New regulations are emerging to secure this approach, for example, in France, the PVID framework regulates the remote acquisition of identity documents and people's faces under the eIDAS regulation. This is required because a new type of digital crime is emerging: deep identity theft. With new deep learning tools, imposters can change their appearance to look like someone else in real time. Imposters can then perform all the common actions required in a remote registration without being detected by identity verification algorithms. Today, smartphone applications and tools for a more limited audience exist allowing imposters to easily transform their appearance in real time. There are even methods to spoof an identity based on a single image of the victim's face. The objective of this thesis is to study the vulnerabilities of remote identity authentication systems against new attacks in order to propose solutions based on deep learning to make the systems more robust
Book chapters on the topic "Face spoofing"
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Wagner, Michael, and Girija Chetty. "Anti-Spoofing: Face." In Encyclopedia of Biometrics, 1–12. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27733-7_67-2.
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Wagner, Michael, and Girija Chetty. "Anti-spoofing, Face." In Encyclopedia of Biometrics, 45–55. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7488-4_67.
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Anjos, André, Ivana Chingovska, and Sébastien Marcel. "Anti-spoofing: Face Databases." In Encyclopedia of Biometrics, 1–13. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27733-7_9067-2.
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Anjos, André, Ivana Chingovska, and Sébastien Marcel. "Anti-spoofing, Face Databases." In Encyclopedia of Biometrics, 55–66. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7488-4_9067.
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Jourabloo, Amin, Yaojie Liu, and Xiaoming Liu. "Face De-spoofing: Anti-spoofing via Noise Modeling." In Computer Vision – ECCV 2018, 297–315. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01261-8_18.
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Anjos, André, Jukka Komulainen, Sébastien Marcel, Abdenour Hadid, and Matti Pietikäinen. "Face Anti-spoofing: Visual Approach." In Handbook of Biometric Anti-Spoofing, 65–82. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6524-8_4.
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Yu, Su-Gyeong, So-Eui kim, Kun Ha Suh, and Eui Chul Lee. "Face Spoofing Detection Using DenseNet." In Intelligent Human Computer Interaction, 229–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68452-5_24.
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Yi, Dong, Zhen Lei, Zhiwei Zhang, and Stan Z. Li. "Face Anti-spoofing: Multi-spectral Approach." In Handbook of Biometric Anti-Spoofing, 83–102. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6524-8_5.
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Hernandez-Ortega, Javier, Julian Fierrez, Aythami Morales, and Javier Galbally. "Introduction to Face Presentation Attack Detection." In Handbook of Biometric Anti-Spoofing, 187–206. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92627-8_9.
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Bhattacharjee, Sushil, Amir Mohammadi, André Anjos, and Sébastien Marcel. "Recent Advances in Face Presentation Attack Detection." In Handbook of Biometric Anti-Spoofing, 207–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92627-8_10.
Full textConference papers on the topic "Face spoofing"
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Reeba, Y. Binny, and R. Shanmugalakshmi. "Spoofing face recognition." In 2015 International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2015. http://dx.doi.org/10.1109/icaccs.2015.7324132.
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Komulainen, Jukka, Abdenour Hadid, and Matti Pietikainen. "Context based face anti-spoofing." In 2013 IEEE 6th International Conference on Biometrics: Theory, Applications and Systems (BTAS). IEEE, 2013. http://dx.doi.org/10.1109/btas.2013.6712690.
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Tang, Ziqi, and Nan Su. "Face anti-spoofing based on face parts segmentation." In 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), edited by Lvqing Yang and Wenjun Tan. SPIE, 2023. http://dx.doi.org/10.1117/12.3004482.
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Parkin, Aleksandr, and Oleg Grinchuk. "Recognizing Multi-Modal Face Spoofing With Face Recognition Networks." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2019. http://dx.doi.org/10.1109/cvprw.2019.00204.
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Marutotamtama, Jane Chrestella, Iwan Setyawan, and Handoko. "Face Recognition and Face Spoofing Detector for Attendance System." In 2022 5th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2022. http://dx.doi.org/10.1109/isriti56927.2022.10052985.
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Agarwal, Akshay, Richa Singh, and Mayank Vatsa. "Face anti-spoofing using Haralick features." In 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, 2016. http://dx.doi.org/10.1109/btas.2016.7791171.
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Phan, Quoc-Tin, Duc-Tien Dang-Nguyen, Giulia Boato, and Francesco G. B. De Natale. "FACE spoofing detection using LDP-TOP." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7532388.
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Liu, Zhao, Zunlei Feng, Zeyu Zou, Rong Zhang, Mingli Song, and Jianping Shen. "Disentangled Representation based Face Anti-Spoofing." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412854.
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Nandy, Anubhab, and Satish Kumar Singh. "Face Spoofing and Presentation Attack Detection." In 2022 IEEE World Conference on Applied Intelligence and Computing (AIC). IEEE, 2022. http://dx.doi.org/10.1109/aic55036.2022.9848980.
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Liu, Ajian, Zichang Tan, Yanyan Liang, and Jun Wan. "Attack-Agnostic Deep Face Anti-Spoofing." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2023. http://dx.doi.org/10.1109/cvprw59228.2023.00674.
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