How far did we get in face spoofing detection?

Souza, Luiz Carlos de; Oliveira, Luciano; Pamplona, Mauricio; Papa, João Paulo

doi:10.1016/j.engappai.2018.04.013

Cited by 44 publications

(26 citation statements)

References 81 publications

(431 reference statements)

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“…Face anti-spoofing is a security method to solve the problem before reaching the recognition phase [3]. The objective of antispoofing is to secure the biometric system from any users with illegal authorization.…”

Section: B Anti-spoofing Methodsmentioning

confidence: 99%

“…A face spoofing case is that a person tries to present fake evidence to the biometric system or face recognition to get the authorization in order to gain resources inside the system in the wrong way [3]. Face spoofing can be implemented by several methods with the help of photographs and videos.…”

Section: A Face Spoofmentioning

confidence: 99%

“…Face spoofing comes with many difficult challenges with the system itself and outside demand in a real-world application. The face spoofing technique trend tends to be in high computational models in order to achieve better accuracy without considering the memory or power cost [3]. The fact that new hardware development is improving each year will provide high computational power and memory to make these systems able to run with real-time performance.…”

Section: Introductionmentioning

confidence: 99%

“…One of many surveys in face spoofing discusses extensive detail [3], making it difficult for newcomers to understand the basic understanding of face spoof. On the contrary, this review discusses general information about face spoofing and the challenge in order to understand the basic knowledge of several approaches to address the spoofing problem.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Review on Face Anti-Spoofing

Perdana

Ardiyanto

Nugroho

2021

IJITEE

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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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Section: B Anti-spoofing Methodsmentioning

confidence: 99%

Section: A Face Spoofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review on Face Anti-Spoofing

Perdana

Ardiyanto

Nugroho

2021

IJITEE

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“…This imaginative innovation shows a great deal of guarantee and could change how we get to delicate data. [1] Be that as it may, as promising as facial acknowledgment seems to be, it has defects. Client photographs can without much of a stretch be found through interpersonal organizations and used to parody facial acknowledgment programming.…”

Section: Introductionmentioning

confidence: 99%

Face Spoofing Detection using Mixed Feature with Deep Convolutional Neural Networks

N V*,

U.,

2020

IJRTE

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Nowadays, face biometric-based access control systems are becoming ubiquitous in daily life while they are still vulnerable to spoofing attacks. Developing robust and reliable methods to prevent such frauds is unavoidable. As deep learning techniques have achieved satisfactory performances in computer vision, they have also been applied to face spoofing detection. However, the numerous parameters in these deep learning-based detection methods cannot be updated to optimum due to limited data. In this paper,a highly accurate face spoof detection system using multiple features and deep learning is proposed. The input video is broken into frames using content-based frame extraction. From each frame, the face of the person is cropped.From the cropped images multiple features like Histogram of Gradients (HoG), Local Binary Pattern (LBP), Center Symmetric LBP (CSLBP), and Gray level co-occurrence Matrix (GLCM) are extracted to train the Convolutional Neural Network(CNN). Training and testing are performed separately by using collected sample data.Experiments on the standard spoof database called Replay-Attack database the proposed system outperform other state-of-the-art techniques, presenting great results in terms of attack detection.

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3T‐FASDM: Linear discriminant analysis‐based three‐tier face anti‐spoofing detection model using support vector machine

Bakshi

Gupta

et al. 2020

Int J Communication

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SummaryIn recent years, to solve the problem of face spoofing, momentous work has been done in this field, but still, there is a need for establishing counter measures to the biometric spoofing attacks. Although trained and evaluated on different databases, impressive results have been achieved in existing face anti‐spoofing techniques, but biometric authentication is a very significant problem as imposters are using lots of reconstructed samples or fake synthetic material or structure that can be used for various attack purposes. For the first time, to the best of our knowledge, this paper explains the security for face anti‐spoofing detection using linear discriminant analysis and validates the results by calculating HTER and accuracy on different databases (i.e., REPLAY ATTACK and CASIA). The proposed model, that is, three‐tier face anti‐spoofing detection model (3T‐FASDM), is used for the detection of the fake biometric user and works well for real‐time applications. The proposed methods tested on a set of state‐of‐the‐art anti‐spoofing features for the face mode gives a very low degree of complexity as 26 general image quality measures are applied to differentiate among legitimate and imposter samples. The outcomes obtained from publically available data show that this technique has improved performance and accuracy by analyzing the HTER and machine learning classifiers that are helpful to differentiate among real and fake traits.

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How far did we get in face spoofing detection?

Cited by 44 publications

References 81 publications

A Review on Face Anti-Spoofing

A Review on Face Anti-Spoofing

Face Spoofing Detection using Mixed Feature with Deep Convolutional Neural Networks

3T‐FASDM: Linear discriminant analysis‐based three‐tier face anti‐spoofing detection model using support vector machine

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