A comparative study on face spoofing attacks

Kumar, Sandeep; Singh, Sukhwinder; Kumar, Jagdish

doi:10.1109/ccaa.2017.8229961

Cited by 60 publications

(23 citation statements)

References 35 publications

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“…In fact, in ISO/IEC 30107 [47], research on sensor attacks related to fingerprint recognition and face recognition has been actively conducted. There is a result that the face recognition system is vulnerable to spoofing attack [48]. In addition, the iris recognition system and the facial recognition system are similar in overall score but score differently on privacy concerns.…”

Section: Discussion In This Section We Analyze the Weights Of Eachmentioning

confidence: 99%

Usability Evaluation Model for Biometric System considering Privacy Concern Based on MCDM Model

Lee

2019

Security and Communication Networks

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Biometric devices play an integral role in consumer’s daily life, providing a seamless environment. However, it is essential to measure the usability of biometrics, owing to the elements of biometrics satisfying both usability and security. This study redefines the elements of biometrics pertaining to usability determined in previous studies and adds elements of psychological relevance, such as privacy concerns. To organize the interrelated usability structure systemically, this paper applies the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) to derive the usability structure. Thereupon, the established structure is applied in the clustered weighted Analytical Network Processes (ANP) to generate the proposed usability evaluation model. By these methods, the pertinent relationships between the factors are clarified and the weight of each element is determined. In the empirical study, 106 students measured usability of the fingerprint recognition system, iris recognition system, and facial recognition system employing our usability evaluation model. The results of this model generate the quantitative score of usability for biometric systems and suggest strategies to increase the score. The proposed usability evaluation model can comprehensively assist usability practitioners to evaluate biometric systems.

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Section: Discussion In This Section We Analyze the Weights Of Eachmentioning

confidence: 99%

Usability Evaluation Model for Biometric System considering Privacy Concern Based on MCDM Model

Lee

2019

Security and Communication Networks

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“…However, biometric presentation attacks can exist alongside the face authentication process. For example, attackers can source photos (photo attacks) or videos (replay attacks) of the person from social networking sites easily and present them to the authentication system [ 102 ]. Remote HR measurement technology can be incorporated to enhance the authentication system [ 103 ].…”

Section: Applicationsmentioning

confidence: 99%

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

Cheng

Wong

Chin

et al. 2021

Sensors

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Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.

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“…These behaviors are all defined as the face spoofing attacks. The face spoofing attacks can be generally divided into three categories, namely photo attacks, video attacks, and 3D mask attacks [21], [22]:…”

Section: Related Work a Face Spoofing Attacksmentioning

confidence: 99%

A Face Spoofing Detection Method Based on Domain Adaptation and Lossless Size Adaptation

2020

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In this paper, a face spoofing detection method called the Fully Convolutional Network with Domain Adaptation and Lossless Size Adaptation (FCN-DA-LSA) is proposed. As its name suggests, the FCN-DA-LSA includes a lossless size adaptation preprocessor followed by an FCN based pixel-level classifier embedded with a domain adaptation layer. The FCN local classifier makes full use of the basic properties of face spoof distortion namely ubiquitous and repetitive. The domain adaptation (DA) layer improves generalization across different domains. The lossless size adaptation (LSA) preserves the highfrequent spoof clues caused by the face recapture process. The ablation study shows that both DA and the LSA are necessary for high-accuracy face spoofing detection. The FCN-LSA obtains competitive performance among the state-of-the-art methods. With the help of small-sample external data in the target domain (2/50, 2/50, and 1/20 subjects for CASIA-FASD, Replay-Attack, and OULU-NPU respectively), the FCN-DA-LSA further improves the performance and outperforms the existing methods.

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A comparative study on face spoofing attacks

Cited by 60 publications

References 35 publications

Usability Evaluation Model for Biometric System considering Privacy Concern Based on MCDM Model

Usability Evaluation Model for Biometric System considering Privacy Concern Based on MCDM Model

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

A Face Spoofing Detection Method Based on Domain Adaptation and Lossless Size Adaptation

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