Anti-Spoofing in Face Recognition: Deep Learning and Image Quality Assessment-Based Approaches

Elloumi, Wael; Chetouani, Aladine; Charrada, Tarek Ben; Fourati, Emna

doi:10.1007/978-3-030-32583-1_4

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…It is difficult to compare performance because each existing approach uses a different testing environment, performance measurements, and datasets. The HTER and testing accuracy noticed in the suggested work provided here using VGG19; however, are better than the regarded existing equivalent attempts from the literature [20,23,24,29] when one compares the explorations carried out utilising datasets NUAA and Replay-Attack (as presented in Table 9 for comparison of existing methods with the proposed approach).…”

Section: Resultsmentioning

confidence: 77%

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Enhanced Face Presentation Attack Prevention Employing Feature Fusion of Pre-trained Deep Convolutional Neural Network Model and Thepade's Sorted Block Truncation Coding

Thepade¹,

Dindorkar²,

Chaudhari³

et al. 2023

IJE

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The evolution and improvements of deep learning are being used to tackle any research obstacles that could be converted into classification problems in all spheres of life. Each Deep convolutional neural network (DCNN) design's output is determined by the depth and value of the hyperparameters, which explains why so many of them have been proposed. These DCNN architectures must be created entirely from scratch, and they can only be used for the applications for which they were intended. Transfer learning may be used to modify these pre-trained networks so they are more appropriate for particular purposes. This article aims to evaluate the empirical performance of the applicability of pre-trained DCNN models to identify human face presentation threats (FPAD). Human FPAD is one of the most significant and crucial areas of research right now because of the introduction of ambient computing, which necessitates contact-free identification of persons with the help of their biometric traits. Six pretrained DCNN models are taken into account for an experimental evaluation in human FPAD alias VGG19, VGG16, DensNet121, MobileNet, Xception, and InceptionV3. The investigation makes use of the NUAA and Replay-Attack benchmark FPAD datasets. Thepade's sorted block truncation coding (SBTC) 10-ary features are merged with deep learning features produced from the finest performing finetuned DCNNs to enhance the FPAD capabilities of analyzed machine learning (ML) classifiers. The integration of features of Thepade's SBTC 10-ary and DCNN has considerably increased the FPAD accuracy of ML classifiers with slightly more computations of feature extraction.

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Section: Resultsmentioning

confidence: 77%

“…Two Presentation Attack Detection (PAD) techniques are developed by Elloumi et al [24] based on deep learning with the quality evaluation of the image. The first strategy uses the LBP histogram computation and VGG16 finetuning, whereas the other strategy relies on Image Quality Measures.…”

Section: Literature Surveymentioning

confidence: 99%

Enhanced Face Presentation Attack Prevention Employing Feature Fusion of Pre-trained Deep Convolutional Neural Network Model and Thepade's Sorted Block Truncation Coding

Thepade¹,

Dindorkar²,

Chaudhari³

et al. 2023

IJE

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“…Some researchers proposed their own models [45] trained from scratch, while others employed pre-trained models like AlexNet [72] and ResNet [81]. In our technique, we used the model introduced by the Oxford Visual Geometry Group (VGG), as this model is widely utilized and provided decent results in many applications [67,[82][83][84][85][86]. More precisely, we fine-tuned the pre-trained VGG16 model without data augmentation, since these treatments change the structure of the data and thus modify the perceived quality [87].…”

Section: Cnn Modelmentioning

confidence: 99%

Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance

Chetouani

Pedersen

2021

Applied Sciences

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An abundance of objective image quality metrics have been introduced in the literature. One important essential aspect that perceived image quality is dependent on is the viewing distance from the observer to the image. We introduce in this study a novel image quality metric able to estimate the quality of a given image without reference for different viewing distances between the image and the observer. We first select relevant patches from the image using saliency information. For each patch, a feature vector is extracted from a convolutional neural network model and concatenated at the viewing distance, for which the quality is predicted. The resulting vector is fed to fully connected layers to predict subjective scores for the considered viewing distance. The proposed method was evaluated using the Colourlab Image Database: Image Quality and Viewing Distance-changed Image Database. Both databases provide subjective scores at two different viewing distances. In the Colourlab Image Database: Image Quality we obtain a Pearson correlation of 0.87 at both 50 cm and 100 cm viewing distances, while in the Viewing Distance-changed Image Database we obtained a Pearson correlation of 0.93 and 0.94 at viewing distance of four and six times the image height. The results show the efficiency of our method and its generalization ability.

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An efficient cybersecurity framework for facial video forensics detection based on multimodal deep learning

Sedik

Faragallah

El‐Sayed

et al. 2021

Neural Comput & Applic

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Anti-Spoofing in Face Recognition: Deep Learning and Image Quality Assessment-Based Approaches

Cited by 3 publications

References 32 publications

Enhanced Face Presentation Attack Prevention Employing Feature Fusion of Pre-trained Deep Convolutional Neural Network Model and Thepade's Sorted Block Truncation Coding

Enhanced Face Presentation Attack Prevention Employing Feature Fusion of Pre-trained Deep Convolutional Neural Network Model and Thepade's Sorted Block Truncation Coding

Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance

An efficient cybersecurity framework for facial video forensics detection based on multimodal deep learning

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