AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The developed model can accurately detect the face and face mask by using the potential of FWGMOA and OERLN and greatly improved the abilities of OERLN by optimizing its structure and training process, leading to the reported gains in accuracy.
A comparative study of three deep learning architectures, namely a Custom Convolutional Neural Network (CNN), VGG16, and MobileNetV2, for face mask detection indicates that lightweight transfer learning models offer an effective and practical solution for real-time face mask detection in resource-constrained environmen...
A comparative analysis of existing studies is presented to highlight the evolution of deep learning techniques and their effectiveness in improving recognition accuracy and computational efficiency and emerging research directions are outlined to provide insights for future research.
A hybrid model which uses several ResNet50 mixed with VGG16 backbones to improve the local facial features with the global context for a robust FER solu-tion, and shows superior robustness to occlusions and pose, even under cross-dataset testing.
Results show that the privacy preserving method introduced in this paper can effectively remove identity-related visual signals while at the same time keeping the level of face detection performance high, a feature which would make it most of all appealing for privacy critical computer vision applications.
The results indicate that the combination of facial-region segmentation, latent feature learning and hybrid Transformer architectures enhances the robustness and generalisation capability of deepfake detection significantly.
A Self supervised Capsule Network (SS-CapsNet) integrates convolutional feature extraction, capsule based learning, dynamic routing and contrastive self supervised learning into a combined framework for robust face recognition.
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