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Relevant bibliographies by topics / Face presentation attack detection

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Journal articles

Academic literature on the topic 'Face presentation attack detection'

Author: Grafiati

Published: 23 September 2023

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Journal articles on the topic "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 perform

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2

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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3

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

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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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5

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 fair

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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 vot

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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 base

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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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