31 results on '"face spoofing"'
Search Results
2. Facial Landmark Features-Based Face Misclassification Detection System
- Author
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Bakshi, Aditya, Gupta, Sunanda, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Singh, Pradeep Kumar, editor, Wierzchoń, Sławomir T., editor, Tanwar, Sudeep, editor, Rodrigues, Joel J. P. C., editor, and Ganzha, Maria, editor
- Published
- 2023
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3. An automated online proctoring system using attentive-net to assess student mischievous behavior.
- Author
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Potluri, Tejaswi, S, Venkatramaphanikumar, and K, Venkata Krishna Kishore
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BLENDED learning ,ONLINE education ,CRIME prevention ,ARTIFICIAL intelligence ,AFFINE transformations ,HUMAN facial recognition software ,POSE estimation (Computer vision) - Abstract
In recent years, the pandemic situation has forced the education system to shift from traditional teaching to online teaching or blended learning. The ability to monitor remote online examinations efficiently is a limiting factor to the scalability of this stage of online evaluation in the education system. Human Proctoring is the most used common approach by either asking learners to take a test in the examination centers or by monitoring visually asking learners to switch on their camera. However, these methods require huge labor, effort, infrastructure, and hardware. This paper presents an automated AI-based proctoring system- 'Attentive system' for online evaluation by capturing the live video of the examinee. Our Attentive system includes four components to estimate the malpractices such as face detection, multiple person detection, face spoofing, and head pose estimation. Attentive Net detects the faces and draws bounding boxes along with confidences. Attentive Net also checks the alignment of the face using the rotation matrix of Affine Transformation. The face net algorithm is combined with Attentive-Net to extract landmarks and facial features. The process for identifying spoofed faces is initiated only for aligned faces by using a shallow CNN Liveness net. The head pose of the examiner is estimated by using the SolvePnp equation, to check if he/she is seeking help from others. Crime Investigation and Prevention Lab (CIPL) datasets and customized datasets with various types of malpractices are used to evaluate our proposed system. Extensive Experimental results demonstrate that our method is more accurate, reliable and robust for proctoring system that can be practically implemented in real time environment as Automated proctoring System. An improved accuracy of 0.87 is reported by authors with the combination of Attentive Net, Liveness net and head pose estimation. [ABSTRACT FROM AUTHOR]
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- 2023
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4. A novel method to enhance color spatial feature extraction using evolutionary time-frequency decomposition for presentation-attack detection.
- Author
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Majeed, Qasim and Fathi, Abdolhossein
- Abstract
Vulnerability to presentation attacks is the most valid issue of face-based authentication systems. Therefore, automatic detection of face spoofing plays a vital role in the safe use of face recognition applications in situations where the system works alone. In this work, we propose a method based on texture feature analysis. We select varying color channels among RGB, HSV, and YCbCr spaces depending on the minimum classification error rate to extract different wavelet sub-bands. Accordingly, the Green (G) channel of the RGB color spaces, the Saturation (S) of the HSV color space, the blue-difference Chroma (Cb) component of the YCbCr color space, and the Grayscale of the facial image to extract wavelet sub-band. The final texture feature vector was constructed using the Local Phase Quantization (LPQ) descriptor on the obtained wavelet sub-bands. Moreover, we use the genetic algorithm to reduce the feature vector's dimensions and minimize the classification error rate. The proposed method's performance was evaluated using inter-dataset and intra-dataset tests on nine public datasets. In these tests, the performance of the proposed method on 3Ddmad, HKBU-MARsV1+, Replay-Mobile, OULU, SiW, WMCA, CASIA-MFS, and MSU-MFSD datasets has proven to be better than the most advanced methods available. [ABSTRACT FROM AUTHOR]
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- 2023
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5. Hierarchical Interpolation of Imagenet Features for Cross-Dataset Presentation Attack Detection
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Singh, Jag Mohan, Ramachandra, Raghavendra, Busch, Christoph, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Yildirim Yayilgan, Sule, editor, Bajwa, Imran Sarwar, editor, and Sanfilippo, Filippo, editor
- Published
- 2021
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6. Face Spoofing Detection Using Dimensionality Reduced Local Directional Pattern and Deep Belief Networks
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Srinivasa Perumal, R., Priya, G. G. Lakshmi, Mouli, P. V. S. S. R. Chandra, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tripathi, Meenakshi, editor, and Upadhyaya, Sushant, editor
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- 2021
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7. Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System.
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Kumar, Sandeep, Rani, Shilpa, Jain, Arpit, Verma, Chaman, Raboaca, Maria Simona, Illés, Zoltán, and Neagu, Bogdan Constantin
- Subjects
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FACIAL expression , *HUMAN fingerprints , *FACE , *BIOMETRY , *AGE , *HOSPITAL personnel - Abstract
Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications. [ABSTRACT FROM AUTHOR]
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- 2022
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8. Learning Deep Feature Representation for Face Spoofing
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Srinivasa Perumal, R., Santosh, K. C., Chandra Mouli, P. V. S. S. R., Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Santosh, K. C., editor, and Hegadi, Ravindra S., editor
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- 2019
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9. Face Spoofing Detection on Low-Power Devices Using Embeddings with Spatial and Frequency-Based Descriptors
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Vareto, Rafael Henrique, Diniz, Matheus A., Schwartz, William Robson, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nyström, Ingela, editor, Hernández Heredia, Yanio, editor, and Milián Núñez, Vladimir, editor
- Published
- 2019
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10. 人脸欺诈检测最新进展及典型方法.
- Author
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胡永健, 王宇飞, 刘贝, 蔡楚鑫, and 冯浩宇
- Abstract
Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2021
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11. Face spoofing detection via ensemble of classifiers toward low-power devices.
- Author
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Vareto, Rafael Henrique and Schwartz, William Robson
- Subjects
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ARTIFICIAL neural networks , *SINGLE-board computers , *SUPPORT vector machines , *FOURIER transforms , *BIOMETRIC identification , *FALSIFICATION of data - Abstract
Facial biometrics tend to be spontaneous, instinctive and less human intrusive. It is regularly employed in the authentication of authorized users and personnel to protect data from violation attacks. A face spoofing attack usually comprises the illegal attempt to access valuable undisclosed information as a trespasser attempts to impersonate an individual holding desirable authentication clearance. In search of such violations, many investigators have devoted their efforts to studying either visual liveness detection or patterns generated during media recapture as predominant indicators to block spoofing violations. This work contemplates low-power devices through the aggregation of Fourier transforms, different classification methods and handcrafted descriptors to estimate whether face samples correspond to falsification attacks. To the best of our knowledge, the proposed method consists of low computational cost and is one of the few methods associating features derived from both spatial and frequency image domains. We conduct experiments on recent and well-known datasets under same and cross-database settings with artificial neural networks, support vector machines and partial least squares ensembles. Results show that although our methodology is geared for resource-limited single-board computers, it can produce significant results, outperforming state-of-the-art approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
12. Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System
- Author
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Sandeep Kumar, Shilpa Rani, Arpit Jain, Chaman Verma, Maria Simona Raboaca, Zoltán Illés, and Bogdan Constantin Neagu
- Subjects
face detection ,U-Net ,Alex-Net ,facial expression ,face spoofing ,age ,Chemical technology ,TP1-1185 - Abstract
Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.
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- 2022
- Full Text
- View/download PDF
13. Motion Analysis Based Cross-Database Voting for Face Spoofing Detection
- Author
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Wu, Lifang, Xu, Yaowen, Jian, Meng, Cai, Wei, Yan, Chuncan, Ma, Yukun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Zhou, Jie, editor, Wang, Yunhong, editor, Sun, Zhenan, editor, Xu, Yong, editor, Shen, Linlin, editor, Feng, Jianjiang, editor, Shan, Shiguang, editor, Qiao, Yu, editor, Guo, Zhenhua, editor, and Yu, Shiqi, editor
- Published
- 2017
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14. Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection.
- Author
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Chen, Haonan, Hu, Guosheng, Lei, Zhen, Chen, Yaowu, Robertson, Neil M., and Li, Stan Z.
- Abstract
Since the human face preserves the richest information for recognizing individuals, face recognition has been widely investigated and achieved great success in various applications in the past decades. However, face spoofing attacks (e.g., face video replay attack) remain a threat to modern face recognition systems. Though many effective methods have been proposed for anti-spoofing, we find that the performance of many existing methods is degraded by illuminations. It motivates us to develop illumination-invariant methods for anti-spoofing. In this paper, we propose a two-stream convolutional neural network (TSCNN), which works on two complementary spaces: RGB space (original imaging space) and multi-scale retinex (MSR) space (illumination-invariant space). Specifically, the RGB space contains the detailed facial textures, yet it is sensitive to illumination; MSR is invariant to illumination, yet it contains less detailed facial information. In addition, the MSR images can effectively capture the high-frequency information, which is discriminative for face spoofing detection. Images from two spaces are fed to the TSCNN to learn the discriminative features for anti-spoofing. To effectively fuse the features from two sources (RGB and MSR), we propose an attention-based fusion method, which can effectively capture the complementarity of two features. We evaluate the proposed framework on various databases, i.e., CASIA-FASD, REPLAY-ATTACK, and OULU, and achieve very competitive performance. To further verify the generalization capacity of the proposed strategies, we conduct cross-database experiments, and the results show the great effectiveness of our method. [ABSTRACT FROM AUTHOR]
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- 2020
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15. Discriminating Between Computer-Generated Facial Images and Natural Ones Using Smoothness Property and Local Entropy
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Nguyen, Huy H., Nguyen-Son, Hoang-Quoc, Nguyen, Thuc D., Echizen, Isao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Shi, Yun-Qing, editor, Kim, Hyoung Joong, editor, Pérez-González, Fernando, editor, and Echizen, Isao, editor
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- 2016
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16. Deep Representations Based on Sparse Auto-Encoder Networks for Face Spoofing Detection
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Yang, Dakun, Lai, Jianhuang, Mei, Ling, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, You, Zhisheng, editor, Zhou, Jie, editor, Wang, Yunhong, editor, Sun, Zhenan, editor, Shan, Shiguang, editor, Zheng, Weishi, editor, Feng, Jianjiang, editor, and Zhao, Qijun, editor
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- 2016
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17. Face Recognition Systems Under Spoofing Attacks
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Chingovska, Ivana, Erdogmus, Nesli, Anjos, André, Marcel, Sébastien, and Bourlai, Thirimachos, editor
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- 2016
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18. A STUDY OF ANTI SPOOFING: VITAL IN FACE RECOGNITION SYSTEMS.
- Author
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JOSHI, TANVI
- Subjects
HUMAN facial recognition software ,FACE - Abstract
Face recognition is the second most widely used biometric approach after fingerprint. The applications of Face recognition are widely accepted and finding their space either at an organization or individual level due its adaptability among the people. But FR systems are vulnerable to spoofing attacks made by non-real human faces. Liveness detection is required for a secure program to protect against such spoofing. In this work, face liveness detection approaches are categorized based on types of techniques used for liveness detection. This classification helps in understanding various spoof attack scenarios and their relation to the formed solutions. A review of the previous works respect to face liveness detection is presented. The main objective is to outline the future development of a new and more secure liveness detection approach for face. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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19. Face Liveness Detection Using a Light Field Camera
- Author
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Sooyeon Kim, Yuseok Ban, and Sangyoun Lee
- Subjects
light field camera ,face spoofing ,face liveness ,microlens image ,sub-aperture image ,Chemical technology ,TP1-1185 - Abstract
A light field camera is a sensor that can record the directions as well as the colors of incident rays. This camera is widely utilized from 3D reconstruction to face and iris recognition. In this paper, we suggest a novel approach for defending spoofing face attacks, like printed 2D facial photos (hereinafter 2D photos) and HD tablet images, using the light field camera. By viewing the raw light field photograph from a different standpoint, we extract two special features which cannot be obtained from the conventional camera. To verify the performance, we compose light field photograph databases and conduct experiments. Our proposed method achieves at least 94.78% accuracy or up to 99.36% accuracy under different types of spoofing attacks.
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- 2014
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20. How far did we get in face spoofing detection?
- Author
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Souza, Luiz, Oliveira, Luciano, Pamplona, Mauricio, and Papa, Joao
- Subjects
- *
HUMAN facial recognition software , *PHISHING , *CYBERTERRORISM , *AUTOMATION , *COMPUTER vision - Abstract
The growing use of control access systems based on face recognition shed light over the need for even more accurate systems to detect face spoofing attacks. In this paper, an extensive analysis on face spoofing detection works published in the last decade is presented. The analyzed works are categorized by their fundamental parts, i.e., descriptors and classifiers. This structured survey also brings a comparative performance analysis of the works considering the most important public data sets in the field. The methodology followed in this work is particularly relevant to observe temporal evolution of the field, trends in the existing approaches, to discuss still opened issues, and to propose new perspectives for the future of face spoofing detection. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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21. Anti-spoofing enabled face recognition based on aggregated local weighted gradient orientation.
- Author
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Beham, M. Parisa and Roomi, S. Md. Mansoor
- Abstract
Spoofing attack is a catastrophic threat for biometric authentication systems. Inspired by the concept of depth map estimation, a novel anti-spoofing technique based on aggregated local weighted gradient orientation (ALWGO) is proposed. We first estimate the depth of the specimen face image. In the next step, highly discriminant ALWGO features are extracted from the depth map. Finally, a sparse representation classifier is trained to distinguish between the genuine and fake faces. This paper particularly addresses the potential of texture gradient features and their variations, on three types of attacks, viz. printed high-definition photographs, warped photographs and videos displayed on mobile phones. The usage of ALWGO features has been extended for further face recognition. Our proposed approach is robust and nonintrusive as compared to many existing methods. Extensive experimental analysis on publicly available databases clearly demonstrates the superiority of our approach for both face spoofing detection and recognition systems. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
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22. Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features.
- Author
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Arashloo, Shervin Rahimzadeh, Kittler, Josef, and Christmas, William
- Abstract
Face recognition has been the focus of attention for the past couple of decades and, as a result, a significant progress has been made in this area. However, the problem of spoofing attacks can challenge face biometric systems in practical applications. In this paper, an effective countermeasure against face spoofing attacks based on a kernel discriminant analysis approach is presented. Its success derives from different innovations. First, it is shown that the recently proposed multiscale dynamic texture descriptor based on binarized statistical image features on three orthogonal planes (MBSIF-TOP) is effective in detecting spoofing attacks, showing promising performance compared with existing alternatives. Next, by combining MBSIF-TOP with a blur-tolerant descriptor, namely, the dynamic multiscale local phase quantization (MLPQ-TOP) representation, the robustness of the spoofing attack detector can be further improved. The fusion of the information provided by MBSIF-TOP and MLPQ-TOP is realized via a kernel fusion approach based on a fast kernel discriminant analysis (KDA) technique. It avoids the costly eigen-analysis computations by solving the KDA problem via spectral regression. The experimental evaluation of the proposed system on different databases demonstrates its advantages in detecting spoofing attacks in various imaging conditions, compared with the existing methods. [ABSTRACT FROM PUBLISHER]
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- 2015
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23. Efficient integration of thermal technology in facial image processing through interspectral synthesis Dissertation
- Author
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Mallat, Khawla, Eurecom [Sophia Antipolis], Sorbonne Université, and Jean-Luc Dugelay
- Subjects
Apprentissage profond ,Face spoofing ,Interspectral synthesis ,Synthèse interspctrale ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,Deep learning ,[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] ,Biométrie faciale ,Facial landmark detection ,Détection des points caractéristiques du visage ,[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR] ,Face biometrics ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,Thermal imagery ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,Usurpation d'identité ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,Imagerie thermique - Abstract
Thermal imaging technology has significantly evolved during the last couple of decades, mostly thanks to thermal cameras having become more affordable and user-friendly. However, and given that the exploration of thermal imagery is reasonably new, only a few public databases are available to the research community. This limitation consequently prevents the impact of deep learning technologies from generating improved and reliable face biometric systems that operate in the thermal spectrum. A possible solution relates to the development of technologies that bridge the gap between the visible and thermal spectrum. In attempting to respond to this necessity, the research presented in this dissertation aims to explore interspectral synthesis as a direction for efficient and prompt integration of thermal technology in already deployed face biometric systems.As a first contribution, a new database, containing paired visible and thermal face images acquired simultaneously, was collected and made publicly available to foster research in thermal face image processing. Motivated by the need for fast and straightforward integration into existing face recognition systems, a set of contributions consisted of proposing a cross-spectrum face recognition framework based on a novel approach of thermal-to-visible face synthesis in order to estimate the visible face from the thermal input. Contributions consisting of exploring interspectral synthesis from visible to thermal spectrum for facial image processing tasks related to, but different than face recognition, are also presented including facial landmark detection and face biometric spoofing in the thermal spectrum.; La technologie de l'imagerie thermique a largement évolué au cours des deux dernières décennies, grâce aux caméras thermiques qui sont devenues plus abordables et simple à utiliser. Cependant, et étant donné que l'exploration de l'imagerie thermique est relativement nouvelle, seules quelques bases de données publiques sont accessibles à la communauté de recherche. Cette limitation empêche donc l'impact des technologies d'apprentissage profond de générer des systèmes fiables de reconnaissance faciale adaptés au spectre thermique. En essayant de surmonter ces contraintes, les travaux de recherche présentés dans ce manuscrit visent à explorer la synthèse interspectrale pour une intégration efficace et rapide de la technologie thermique dans les systèmes de biométire faciale existants. Comme première contribution, une nouvelle base de données, contenant des paires d'images de visages visibles et thermiques acquises simultanément, a été collectée et mise en public afin de favoriser la recherche dans le domaine de l’imagerie thermique de visage. Motivé par le besoin d'une intégration simple dans les systèmes de biométrie faciale existants, un ensemble de contributions a proposé un cadre de reconnaissance faciale cross-spectral basé sur une nouvelle approche de synthèse des visages afin d'estimer le visage visible à partir d’un visage thermique. Autres contributions consistant à explorer la synthèse interspectrale, du spectre visible au spectre thermique, pour des tâches de traitement d'images faciales liées à la reconnaissance faciale, sont également présentées notamment la détection des points caractéristiques de visage et l'usurpation d’identité dans le spectre thermique.
- Published
- 2020
24. Learning Generalized Deep Feature Representation for Face Anti-Spoofing
- Author
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Xinghao Jiang, Peisong He, Anderson Rocha, Haoliang Li, Shiqi Wang, Alex C. Kot, and School of Electrical and Electronic Engineering
- Subjects
021110 strategic, defence & security studies ,Spoofing attack ,Computer Networks and Communications ,Computer science ,Generalization ,business.industry ,Deep learning ,0211 other engineering and technologies ,Pattern recognition ,02 engineering and technology ,Convolutional neural network ,Deep Learning ,Discriminative model ,Feature (computer vision) ,Face (geometry) ,Electrical and electronic engineering [Engineering] ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Safety, Risk, Reliability and Quality ,Representation (mathematics) ,business ,Face Spoofing - Abstract
In this paper, we propose a novel framework leveraging the advantages of the representational ability of deep learning and domain generalization for face spoofing detection. In particular, the generalized deep feature representation is achieved by taking both spatial and temporal information into consideration, and a 3D convolutional neural network architecture tailored for the spatial-temporal input is proposed. The network is first initialized by training with augmented facial samples based on cross-entropy loss and further enhanced with a specifically designed generalization loss, which coherently serves as the regularization term. The training samples from different domains can seamlessly work together for learning the generalized feature representation by manipulating their feature distribution distances. We evaluate the proposed framework with different experimental setups using various databases. Experimental results indicate that our method can learn more discriminative and generalized information compared with the state-of-the-art methods. NRF (Natl Research Foundation, S’pore)
- Published
- 2018
25. Monocular camera-based face liveness detection by combining eyeblink and scene context.
- Author
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Pan, Gang, Sun, Lin, Wu, Zhaohui, and Wang, Yueming
- Abstract
This paper presents a face liveness detection system against spoofing with photographs, videos, and 3D models of a valid user in a face recognition system. Anti-spoofing clues inside and outside a face are both exploited in our system. The inside-face clues of spontaneous eyeblinks are employed for anti-spoofing of photographs and 3D models. The outside-face clues of scene context are used for anti-spoofing of video replays. The system does not need user collaborations, i.e. it runs in a non-intrusive manner. In our system, the eyeblink detection is formulated as an inference problem of an undirected conditional graphical framework which models contextual dependencies in blink image sequences. The scene context clue is found by comparing the difference of regions of interest between the reference scene image and the input one, which is based on the similarity computed by local binary pattern descriptors on a series of fiducial points extracted in scale space. Extensive experiments are carried out to show the effectiveness of our system. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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26. How far did we get in face spoofing detection?
- Author
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Luciano Oliveira, Mauricio Pamplona, Luiz Enrique Vieira de Souza, João Paulo Papa, Universidade Federal da Bahia (UFBA), and Universidade Estadual Paulista (Unesp)
- Subjects
FOS: Computer and information sciences ,021110 strategic, defence & security studies ,Face spoofing ,Spoofing attack ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,0211 other engineering and technologies ,Computer Science - Computer Vision and Pattern Recognition ,02 engineering and technology ,Computer security ,computer.software_genre ,Facial recognition system ,Data science ,Field (computer science) ,Artificial Intelligence ,Control and Systems Engineering ,Face (geometry) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Face recognition ,Electrical and Electronic Engineering ,Survey ,computer - Abstract
Made available in DSpace on 2018-11-26T16:01:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-06-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) The growing use of control access systems based on face recognition shed light over the need for even more accurate systems to detect face spoofing attacks. In this paper, an extensive analysis on face spoofing detection works published in the last decade is presented. The analyzed works are categorized by their fundamental parts, i.e., descriptors and classifiers. This structured survey also brings a comparative performance analysis of the works considering the most important public data sets in the field. The methodology followed in this work is particularly relevant to observe temporal evolution of the field, trends in the existing approaches, to discuss still opened issues, and to propose new perspectives for the future of face spoofing detection. Univ Fed Bahia, IVISION Lab, Salvador, BA, Brazil Sao Paulo State Univ, RECOGNA Lab, Sao Paulo, Brazil Sao Paulo State Univ, RECOGNA Lab, Sao Paulo, Brazil FAPESP: 2013/07375-0 FAPESP: 2014/12236-1 FAPESP: 2016/19403-6 CNPq: 306166/2014-3
- Published
- 2017
27. Face Liveness Detection Using a Light Field Camera
- Author
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Soo Yeon Kim, Sangyoun Lee, and Yuseok Ban
- Subjects
Spoofing attack ,Computer science ,Iris recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,lcsh:Chemical technology ,Biochemistry ,Article ,Analytical Chemistry ,law.invention ,Camera auto-calibration ,law ,light field camera ,sub-aperture image ,microlens image ,Computer vision ,lcsh:TP1-1185 ,Electrical and Electronic Engineering ,Instrumentation ,Light-field camera ,face spoofing ,business.industry ,3D reconstruction ,Atomic and Molecular Physics, and Optics ,Face (geometry) ,Artificial intelligence ,face liveness ,business ,Light field ,Camera resectioning - Abstract
A light field camera is a sensor that can record the directions as well as the colors of incident rays. This camera is widely utilized from 3D reconstruction to face and iris recognition. In this paper, we suggest a novel approach for defending spoofing face attacks, like printed 2D facial photos (hereinafter 2D photos) and HD tablet images, using the light field camera. By viewing the raw light field photograph from a different standpoint, we extract two special features which cannot be obtained from the conventional camera. To verify the performance, we compose light field photograph databases and conduct experiments. Our proposed method achieves at least 94.78% accuracy or up to 99.36% accuracy under different types of spoofing attacks.
- Published
- 2014
- Full Text
- View/download PDF
28. Reflectance analysis based countermeasure technique to detect face mask attacks
- Author
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Kose, Neslihan, Dugelay, Jean-Luc, Kose, Neslihan, and Dugelay, Jean-Luc
- Subjects
face spoofing ,mask attacks ,countermeasure - Published
- 2013
29. The 2nd competition on counter measures to 2D face spoofing attacks
- Author
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Alexander Nouak, Tarun Krishna, Nicola Sirena, Anderson Rocha, Shubham Gupta, Dushyant Goyal, Helio Pedrini, Muhammad Adeel Waris, Moncef Gabbouj, Javier Galbally, Arjan Kuijper, Zhen Lei, Ivana Chingovska, Serkan Kiranyaz, J. Ficrrcz, Honglei Zhang, Shubham Bansal, Jukka Komulainen, André Anjos, Maurizio Pili, Iftikhar Ahmad, Dong Yi, Naser Damer, Fabio Roli, Stan Z. Li, O. Kahm, Ayush K. Rai, Jianwei Yang, W. S. Schwartz, C. Glaser, Allan Pinto, Roberto Tronci, Shubham Khandelwal, Tiago de Freitas Pereira, Sébastien Marcel, UAM. Departamento de Tecnología Electrónica y de las Comunicaciones, and Análisis y Tratamiento de Voz y Señales Biométricas (ING EPS-002)
- Subjects
Spoofing attack ,Biometrics ,Computer science ,Internet privacy ,Security of data ,0211 other engineering and technologies ,Face (sociological concept) ,02 engineering and technology ,Anti-spoofing ,Computer security ,computer.software_genre ,Facial recognition system ,Competition (economics) ,0202 electrical engineering, electronic engineering, information engineering ,Face recognition ,Replay attack ,face spoofing ,Telecomunicaciones ,021110 strategic, defence & security studies ,Modality (human–computer interaction) ,Competition ,business.industry ,Counter-Measures ,presentation attack ,Variety (cybernetics) ,020201 artificial intelligence & image processing ,business ,computer - Abstract
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. I. Chingovska, J. Yang, Z. Lei, D. Yi, S. Z. Li, O. Kahm, C. Glaser, N. Damer, A. Kuijper, A. Nouak, J. Komulainen, T. Pereira, S. Gupta, S. Khandelwal, S. Bansal, A. Rai, T. Krishna, D. Goyal, M.-A. Waris, H. Zhang, I. Ahmad, S. Kiranyaz, M. Gabbouj, R. Tronci, M. Pili, N. Sirena, F. Roli, J. Galbally, J. Fiérrez, A. Pinto, H. Pedrini, W. S. Schwartz, A. Rocha, A. Anjos, S. Marcel, "The 2nd competition on counter measures to 2D face spoofing attacks" in International Conference on Biometrics (ICB), Madrid (Spain), 2013, 1-6, As a crucial security problem, anti-spoofing in biometrics, and particularly for the face modality, has achieved great progress in the recent years. Still, new threats arrive inform of better, more realistic and more sophisticated spoofing attacks. The objective of the 2nd Competition on Counter Measures to 2D Face Spoofing Attacks is to challenge researchers to create counter measures effectively detecting a variety of attacks. The submitted propositions are evaluated on the Replay-Attack database and the achieved results are presented in this paper., The authors would like to thank the Swiss Innovation Agency (CTI Project Replay) and the FP7 European TABULA RASA Project4 (257289) for their financial support.
- Published
- 2013
30. Countermeasure for the protection of face recognition systems against mask attacks
- Author
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Kose, Neslihan, Dugelay, Jean-Luc, Kose, Neslihan, and Dugelay, Jean-Luc
- Subjects
face spoofing ,mask attacks ,countermeasure - Published
- 2013
31. Moving Face Spoofing Detection via 3D Projective Invariants
- Author
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Maria De Marsico, Jean-Luc Dugelay, Daniel Riccio, Michele Nappi, Maria De, Marsico, Michele, Nappi, Riccio, Daniel, and Jean Luc, Dugelay
- Subjects
face spoofing ,Spoofing attack ,Biometrics ,business.industry ,Computer science ,3d geometric invariants ,spoofing ,face recognition ,Facial recognition system ,Object detection ,geometric invariants ,Object-class detection ,Face (geometry) ,Computer vision ,Artificial intelligence ,business ,Face detection ,Replay attack - Abstract
Face recognition provides many advantages compared with other available biometrics, but it is particularly subject to spoofing. The most accurate methods in literature addressing this problem, rely on the estimation of the three-dimensionality of faces, which heavily increase the whole cost of the system. This paper proposes an effective and efficient solution to problem of face spoofing. Starting from a set of automatically located facial points, we exploit geometric invariants for detecting replay attacks. The presented results demonstrate the effectiveness and efficiency of the proposed indices.
- Published
- 2012
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