A comprehensive survey on techniques to handle face identity threats: challenges and opportunities

Rusia, Mayank Kumar; Singh, Dushyant Kumar

doi:10.1007/s11042-022-13248-6

Cited by 36 publications

(11 citation statements)

References 176 publications

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“…Image enhancement includes various preprocessing operations such as serialization and annotation, resizing, and class-wise labeling of the dataset. Applying these operations reduces the inconsistency and complexity of our use case NUAA dataset [12,13].…”

Section: Image Enhancementmentioning

confidence: 99%

A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks

Rusia

Singh

2022

Cybernetics and Information Technologies

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Given the face spoofing attack, adequate protection of human identity through face has become a significant challenge globally. Face spoofing is an act of presenting a recaptured frame before the verification device to gain illegal access on behalf of a legitimate person with or without their concern. Several methods have been proposed to detect face spoofing attacks over the last decade. However, these methods only consider the luminance information, reflecting poor discrimination of spoofed face from the genuine face. This article proposes a practical approach combining Local Binary Patterns (LBP) and convolutional neural network-based transfer learning models to extract low-level and high-level features. This paper analyzes three color spaces (i.e., RGB, HSV, and YCrCb) to understand the impact of the color distribution on real and spoofed faces for the NUAA benchmark dataset. In-depth analysis of experimental results and comparison with other existing approaches show the superiority and effectiveness of our proposed models.

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Section: Image Enhancementmentioning

confidence: 99%

A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks

Rusia

Singh

2022

Cybernetics and Information Technologies

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“…Face recognition is now common in daily life, enabling smartphone unlocking [1], auto-tagging on social media [2], identity verification at airports [3], and others application. Despite its extensive uses, face recognition faces challenges such as recognizing faces in low resolution [4], varied poses [5], and different lighting conditions [6]. These issues can reduce performance [7], requiring enhancements for better effectiveness and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of pseudorandom pixel placement in enhancing face recognition using VGG-Face

Hadis,

Akita,

Toda

2024

Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023)

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In efforts to enhance face recognition performance, techniques ranging from super-resolution methods to the use of Local Binary Pattern (LBP) and deep learning have been explored. Among these, the pseudorandom pixel placement (PSE) technique has demonstrated potential in face recognition. Nevertheless, its testing was previously limited to just 8 subjects. This study undertakes a comprehensive evaluation of the PSE technique with a larger sample, utilizing 2000 subjects from the DigiFACE1M dataset and leveraging the state-of-the-art VGG-Face deep learning model. Through experiments involving 10 different PSE patterns on 144.000 face images, our findings indicate that, compared to Regular Pixel Placement (REG), PSE achieved an improvement in average accuracy by 1.05%, reduced the standard deviation by 1.47%, and resulted in 31 additional subjects achieving 100% accuracy. We conclude that PSE consistently outperforms REG in face recognition tasks using the VGG-Face model across the majority of tested scenarios.

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“…For face image generation, a GAN is trained on a dataset of real face images. 11 The generator starts by creating low-resolution, blurry images, but as training progresses, it learns to create more detailed, realistic images on the basis of style images. The face image generation process is done in two ways.…”

Section: Introductionmentioning

confidence: 99%

“…Based on all these facts, we can say that the GAN models with proper optimization are suitable deep learning models to generate realistic random faces for automated Multimedia content creation. For face image generation, a GAN is trained on a dataset of real face images 11 . The generator starts by creating low‐resolution, blurry images, but as training progresses, it learns to create more detailed, realistic images on the basis of style images.…”

Section: Introductionmentioning

confidence: 99%

Diversified realistic face image generationGANfor human subjects in multimedia content creation

Kumar,

Singh

2024

Computer Animation & Virtual

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Face image generation plays an important role in generating innovative and unique multimedia content using the GAN model. With these qualities of the GAN model, they have numerous challenges in the human face image generation. The problems encountered in the generation of facial images are like blurriness in images, incomplete details in the generated facial images, high computational power requirements, and so forth. In this manuscript, we proposed a GAN model that utilizes the composite strength of VGG‐16 and ResNet‐50's models to overcome those difficulties. It uses VGG‐16 to build a discriminator model to discriminate between real and fake images. The generator model utilizes a combination of components from the ResNet‐50 and VGG‐16 models to enhance the image generation process at each iteration, resulting in the creation of realistic face images. The proposed DRFI GAN (Diversified and Realistic Face Image Generation GAN) model's generator achieves an impressive low FID score of 20.50, which is less than existing state‐of‐the‐art approaches. Furthermore, our findings indicate that the images generated by the DRFI GAN model exhibit 10%–15% greater efficiency and realism with reduced training time compared to existing state‐of‐the‐art methods with lower FID scores.

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A comprehensive survey on techniques to handle face identity threats: challenges and opportunities

Cited by 36 publications

References 176 publications

A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks

A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks

Evaluation of pseudorandom pixel placement in enhancing face recognition using VGG-Face

Diversified realistic face image generationGANfor human subjects in multimedia content creation

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