Face Spoof Detection Using VGG-Face Architecture

Balamurali, K; Chandru, S.; Razvi, Muhammed Sohail; Kumar, Vikas

doi:10.1088/1742-6596/1917/1/012010

Cited by 7 publications

(3 citation statements)

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“…The authors [8] have proposed a system based on YCBCR and CIELUV color space that uses VGG-Face architecture for spoof detection. The authors denoised the face images and converted them to the above color space before passing them to the VGG-Face.…”

Section: Related Workmentioning

confidence: 99%

“…The VGG-16 [8] architecture consists of 16 layers in all with 13 Convolution and 3 fully connected layers which is trained on 1000 different classes and best used for image classification tasks. VGG-16 architecture with fine-tuning is shown below in Figure 2; it has 13 Convolution layers with 5 max-pooling layers & 2 dense layers,1 Flatten and 1 Dropout layer; the total count of layers is 21.…”

Section: Vgg-16 Fine-tunedmentioning

confidence: 99%

“…The paper presents Comparison of two pre-trained architectures for detecting 2D and 3D attacks. The VGG-16 [8] and ResNet-50 [6] model is fine-tuned and applied to the 2D and 3D attack datasets. The results are evaluated based on the standard performance metrics and compared with the existing systems proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Fine-Tuned Networks on Generalization for Face Spoofing Detection

Shinde,

Thepade,

Bongale

et al. 2024

RIA

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Spoofing is a primary security concern for all the organizations and researchers across the globe. Security can be achieved through different mediums; authentication is one such important medium. Biometric Authentication is considered as an important and strong form that's difficult to break. Biometric authentication mainly includes two mechanisms, viz. Physiological and Behavioral, Physiological traits include the face, fingerprint, retina, iris, palm geometry, etc. Face Recognition has many application areas due to its ease of implementation, and they can be easily fooled or spoofed, termed as Face Spoofing Attack. Face spoofing attacks are viz. 2D and 3D attacks, 2D Attacks include Fake photo, Warped photos, Video display and 3D attacks performed using 3D masks. Deep learning methods have proved beneficial for detecting spoofing attacks; these methods use fine-tuned and pre-trained models. The paper compares the proposed fine-tuned VGG16 and RESNET-50 architectures and their generalization performance of Face Spoofing Detection. The 3D MAD and NUAA Imposter Dataset are used to validate the performance for two color spaces viz. RGB and YCBCR; the results are obtained for both color spaces. RGB color space is related to human visual system but it's not invariant to illumination on the other hand YCBCR separates chrominance and luminance part which makes it illumination invariant and face recognition systems have reflectance issue. Cross-dataset evaluation is an important metric for face liveness detection. The paper presents cross dataset results on the above datasets with the lowest HTER of 18%. The fine-tuned VGG-16 architecture gives the best values for cross-dataset evaluation when trained on 3D MAD and tested for NUAA imposter dataset and same is true for RESNET-50 architecture.

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Section: Related Workmentioning

confidence: 99%

Section: Vgg-16 Fine-tunedmentioning

confidence: 99%