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Convolutional neural networks (CNNs) with deep learning have recently achieved a remarkable success with a superior performance in computer vision applications. Most of CNN-based methods extract image features at the last layer using a single CNN architecture with orderless quantization approaches, which limits the utilization of intermediate convolutional layers for identifying image local patterns. As one of the first works in the context of content-based image retrieval (CBIR), this paper proposes a new bilinear CNN-based architecture using two parallel CNNs as feature extractors. The activations of convolutional layers are directly used to extract the image features at various image locations and scales. The network architecture is initialized by deep CNNs sufficiently pre-trained on large generic image dataset then fine-tuned for the CBIR task. Additionally, an efficient bilinear root pooling is proposed and applied to the low-dimensional pooling layer to reduce the dimension of image features to compact but high discriminative image descriptors. Finally, an endto-end training with backpropagation is performed to fine-tune the final architecture and to learn its parameters for the image retrieval task. The experimental results achieved on three standard benchmarking image datasets demonstrate the outstanding performance of the proposed architecture at extracting and learning complex features for the CBIR task without prior knowledge about the semantic meta-data of images. For instance, using a very compact image vector of 16-length, we achieve retrieval accuracy 95.7% (mAP) on Oxford5K and 88.6% on Oxford105K; which outperforms the best results reported by state-of-the-art approaches. Additionally, a noticeable reduction is attained in the required extraction time for image features and the memory size required for storage.

A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR.

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