Face Anti-Spoofing Using CNN Classifier &amp; Face liveness Detection

Hadiprakoso, Raden Budiarto; Setiawan, Hermawan; Girinoto,

doi:10.1109/icoiact50329.2020.9331977

Cited by 18 publications

(9 citation statements)

References 12 publications

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“…After inputting the thermal imaging face into the detection model, the probability of the face belonging to the real body 𝑃 and the probability of the face belonging to the fake 𝑃 can be obtained. The original classification strategy is shown in (1). result = real, if P > P fake, else…”

Section: Optimization Strategymentioning

confidence: 99%

See 1 more Smart Citation

Lightweight face liveness detection method based on 32×24 thermal imaging images and ShuffleNet

Zhang,

Xie,

et al. 2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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This paper aims to solve the problem of deploying face liveness detection methods on mobile terminals with low computing resources. A lightweight face liveness detection method based on 32 × 24 thermal imaging images and ShuffleNet is proposed. This method can be deployed on mobile devices with low computing resources on the premise of ensuring detection accuracy. In addition, this paper also proposes an optimization strategy, which can completely eliminate the situation that the fake face is misidentified as a real face, and at the same time control the probability of the real face misidentified as a fake face at a very low level. The experimental results show that the proposed method has high practical value.

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Section: Optimization Strategymentioning

confidence: 99%

“…Biological feature-based detection uses biological information of a real face. Hadiprakoso et al [1] . proposes a combined method of face liveness detection and CNN classifier, including a blinking eye module to evaluate eye openness and lip movement, and a convolutional neural network (CNN) classifier module.…”

Section: Introductionmentioning

confidence: 99%

Lightweight face liveness detection method based on 32×24 thermal imaging images and ShuffleNet

Zhang,

Xie,

et al. 2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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“…In terms of attack variants, it consists only of photo attacks. Despite these facts, NUAA remains popular among FPAD researchers [49][50][51].…”

Section: Datasetsmentioning

confidence: 99%

Deep Transfer Learning on the Aggregated Dataset for Face Presentation Attack Detection

et al. 2022

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Presentation attacks are becoming a serious threat to one of the most common biometric applications, namely face recognition (FR). In recent years, numerous methods have been presented to detect and identify these attacks using publicly available datasets. However, such datasets are often collected in controlled environments and are focused on one specific type of attack. We hypothesise that a model’s accurate performance on one or more public datasets does not necessarily guarantee generalisation across other, unseen face presentation attacks. To verify our hypothesis, in this paper, we present an experimental framework where the generalisation ability of pre-trained deep models is assessed using four popular and commonly used public datasets. Extensive experiments were carried out using various combinations of these datasets. Results show that, in some circumstances, a slight improvement in model performance can be achieved by combining different datasets for training purposes. However, even with a combination of public datasets, models still could not be trained to generalise to unseen attacks. Moreover, models could not necessarily generalise to a learned format of attack over different datasets. The work and results presented in this paper suggest that more diverse datasets are needed to drive this research as well as the need for devising new methods capable of extracting spoof-specific features which are independent of specific datasets.

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“…The results depicted the supremacy of the established technique over the traditional methods for detecting the face spoofing. Raden Budiarto Hadiprakoso, et.al (2020) projected a combined technique detecting face liveness and CNN (Convolutional Neural Network) classification algorithm [19]. Two modules namely blinking eye module to compute eye openness and lip movement, and the CCN classification module were involved in this technique.…”

Section: B Face Spoof Detection Using Hybrid Techniquementioning

confidence: 99%

Hybrid Algorithm for Face Spoof Detection

Mittal¹,

Kaur²,

Oberoi³

2022

IJRASET

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The face spoof detection is the approach which can detect spoofed face. The face spoof detection methods has various phases which include pre-processing, feature extraction and classification. The classification algorithm can classify into two classes which are spoofed or not spoofed. The KNN approach is used previously with the GLCM algorithm for the face spoof detection which give low accuracy. In this research work, the hybrid classification method is proposed which is the combination of random forest, k nearest neighbour and SVM Classifiers. The simulation outcomes depict that the introduced method performs more efficiently in comparison with the conventional techniques with regard to accuracy. Keywords: Face Spoof, KNN, Hybrid Classifier, GLCM

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Face Anti-Spoofing Using CNN Classifier & Face liveness Detection

Cited by 18 publications

References 12 publications

Lightweight face liveness detection method based on 32×24 thermal imaging images and ShuffleNet

Lightweight face liveness detection method based on 32×24 thermal imaging images and ShuffleNet

Deep Transfer Learning on the Aggregated Dataset for Face Presentation Attack Detection

Hybrid Algorithm for Face Spoof Detection

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