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Gotowe bibliografie tematyczne / Face presentation attack detection

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Gotowa bibliografia na temat „Face presentation attack detection”

Autor: Grafiati

Data publikacji: 23 września 2023

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Artykuły w czasopismach na temat "Face presentation attack detection"

1

Abdullakutty, Faseela, Pamela Johnston, and Eyad Elyan. "Fusion Methods for Face Presentation Attack Detection." Sensors 22, no. 14 (2022): 5196. http://dx.doi.org/10.3390/s22145196.

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Face presentation attacks (PA) are a serious threat to face recognition (FR) applications. These attacks are easy to execute and difficult to detect. An attack can be carried out simply by presenting a video, photo, or mask to the camera. The literature shows that both modern, pre-trained, deep learning-based methods, and traditional hand-crafted, feature-engineered methods have been effective in detecting PAs. However, the question remains as to whether features learned in existing, deep neural networks sufficiently encompass traditional, low-level features in order to achieve optimal performance on PA detection tasks. In this paper, we present a simple feature-fusion method that integrates features extracted by using pre-trained, deep learning models with more traditional colour and texture features. Extensive experiments clearly show the benefit of enriching the feature space to improve detection rates by using three common public datasets, namely CASIA, Replay Attack, and SiW. This work opens future research to improve face presentation attack detection by exploring new characterizing features and fusion strategies.

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Zhu, Shuaishuai, Xiaobo Lv, Xiaohua Feng, Jie Lin, Peng Jin, and Liang Gao. "Plenoptic Face Presentation Attack Detection." IEEE Access 8 (2020): 59007–14. http://dx.doi.org/10.1109/access.2020.2980755.

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Kowalski, Marcin. "A Study on Presentation Attack Detection in Thermal Infrared." Sensors 20, no. 14 (2020): 3988. http://dx.doi.org/10.3390/s20143988.

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Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The aim of this paper is to present outcomes of investigations on the detection of various face presentation attacks in thermal infrared in various conditions including thermal heating of masks and various states of subjects. A thorough analysis of presentation attacks using printed and displayed facial photographs, 3D-printed, custom flexible 3D-latex and silicone masks is provided. The paper presents the intensity analysis of thermal energy distribution for specific facial landmarks during long-lasting experiments. Thermalization impact, as well as varying the subject’s state due to physical effort on presentation attack detection are investigated. A new thermal face spoofing dataset is introduced. Finally, a two-step deep learning-based method for the detection of presentation attacks is presented. Validation results of a set of deep learning methods across various presentation attack instruments are presented.

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4

Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z. Li. "Multi-Modal Face Presentation Attack Detection." Synthesis Lectures on Computer Vision 9, no. 1 (2020): 1–88. http://dx.doi.org/10.2200/s01032ed1v01y202007cov017.

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Alshareef, Norah, Xiaohong Yuan, Kaushik Roy, and Mustafa Atay. "A Study of Gender Bias in Face Presentation Attack and Its Mitigation." Future Internet 13, no. 9 (2021): 234. http://dx.doi.org/10.3390/fi13090234.

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In biometric systems, the process of identifying or verifying people using facial data must be highly accurate to ensure a high level of security and credibility. Many researchers investigated the fairness of face recognition systems and reported demographic bias. However, there was not much study on face presentation attack detection technology (PAD) in terms of bias. This research sheds light on bias in face spoofing detection by implementing two phases. First, two CNN (convolutional neural network)-based presentation attack detection models, ResNet50 and VGG16 were used to evaluate the fairness of detecting imposer attacks on the basis of gender. In addition, different sizes of Spoof in the Wild (SiW) testing and training data were used in the first phase to study the effect of gender distribution on the models’ performance. Second, the debiasing variational autoencoder (DB-VAE) (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) was applied in combination with VGG16 to assess its ability to mitigate bias in presentation attack detection. Our experiments exposed minor gender bias in CNN-based presentation attack detection methods. In addition, it was proven that imbalance in training and testing data does not necessarily lead to gender bias in the model’s performance. Results proved that the DB-VAE approach (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) succeeded in mitigating bias in detecting spoof faces.

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6

Benlamoudi, Azeddine, Salah Eddine Bekhouche, Maarouf Korichi, et al. "Face Presentation Attack Detection Using Deep Background Subtraction." Sensors 22, no. 10 (2022): 3760. http://dx.doi.org/10.3390/s22103760.

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Currently, face recognition technology is the most widely used method for verifying an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face presentation attacks, in which a photo or video of an authorized person’s face is used to obtain access to services. Based on a combination of background subtraction (BS) and convolutional neural network(s) (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face presentation attack detection algorithm. This algorithm includes a fully connected (FC) classifier with a majority vote (MV) algorithm, which uses different face presentation attack instruments (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the face anti-spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results are very interesting and are much better than those obtained by state-of-the-art methods. For instance, on the REPLAY-ATTACK database, we were able to attain a half-total error rate (HTER) of 0.62% and an equal error rate (EER) of 0.58%. We attained an EER of 0% on both the CASIA-FASD and the MSU MFSD databases.

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7

Wan, Jun, Sergio Escalera, Hugo Jair Escalante, Guodong Guo, and Stan Z. Li. "Special Issue on Face Presentation Attack Detection." IEEE Transactions on Biometrics, Behavior, and Identity Science 3, no. 3 (2021): 282–84. http://dx.doi.org/10.1109/tbiom.2021.3089903.

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8

Nguyen, Dat, Tuyen Pham, Min Lee, and Kang Park. "Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information." Sensors 19, no. 2 (2019): 410. http://dx.doi.org/10.3390/s19020410.

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Face-based biometric recognition systems that can recognize human faces are widely employed in places such as airports, immigration offices, and companies, and applications such as mobile phones. However, the security of this recognition method can be compromised by attackers (unauthorized persons), who might bypass the recognition system using artificial facial images. In addition, most previous studies on face presentation attack detection have only utilized spatial information. To address this problem, we propose a visible-light camera sensor-based presentation attack detection that is based on both spatial and temporal information, using the deep features extracted by a stacked convolutional neural network (CNN)-recurrent neural network (RNN) along with handcrafted features. Through experiments using two public datasets, we demonstrate that the temporal information is sufficient for detecting attacks using face images. In addition, it is established that the handcrafted image features efficiently enhance the detection performance of deep features, and the proposed method outperforms previous methods.

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9

Ramachandra, Raghavendra, and Christoph Busch. "Presentation Attack Detection Methods for Face Recognition Systems." ACM Computing Surveys 50, no. 1 (2017): 1–37. http://dx.doi.org/10.1145/3038924.

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10

Peng, Fei, Le Qin, and Min Long. "Face presentation attack detection using guided scale texture." Multimedia Tools and Applications 77, no. 7 (2017): 8883–909. http://dx.doi.org/10.1007/s11042-017-4780-0.

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