An evaluation of super-resolution for face recognition

We evaluate the performance of face recognition algorithms on images at various resolutions. Then we show to what extent super-resolution (SR) methods can improve the recognition performance when comparing low-resolution (LR) to high-resolution (HR) facial images. Our experiments use both synthetic data (from the FRGC v1.0 database) and surveillance images (from the SCface database). Three face recognition methods are used, namely Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Patterns (LBP). Two SR methods are evaluated. The first method learns the mapping between LR images and the corresponding HR images using a regression model. As a result, the reconstructed SR images are close to the HR images that belong to the same subject and far away from others. The second method compares LR and HR facial images without explicitly constructing SR images. It finds a coherent feature space where the correlation of LR and HR is maximum, and then compute the mapping from LR to HR in this feature space. The performance of the two SR methods are compared to that delivered by the standard face recognition without SR. The results show that LDA is mostly robust to resolution changes while LBP is not suitable for the recognition of LR images. SR methods improve the recognition accuracy when downsampled images are used and the first method provides better results than the second one. However, the improvement for realistic LR surveillance images remains limited

A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution

Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets are being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research

Super-Resolution in Still Images and Videos via Deep Learning

PhDThe evolution of multimedia systems and technology in the past decade has enabled production and delivery of visual content in high resolution, and the thirst for achieving higher de nition pictures with more detailed visual characteristics continues. This brings attention to a critical computer vision task for spatial up-sampling of still images and videos called super-resolution. Recent advances in machine learning, and application of deep neural networks, have resulted in major improvements in various computer vision applications. Super-resolution is not an exception, and it is amongst the popular topics that have been a ected signi cantly by the emergence of deep learning. Employing modern machine learning solutions has made it easier to perform super-resolution in both images and videos, and has allowed professionals from di erent elds to upgrade low resolution content to higher resolutions with visually appealing picture delity. In spite of that, there remain many challenges to overcome in adopting deep learning concepts for designing e cient super-resolution models. In this thesis, the current trends in super-resolution, as well as the state of the art are presented. Moreover, several contributions for improving the performance of the deep learning-based super-resolution models are described in detail. The contributions include devising theoretical approaches, as well as proposing design choices that can lead to enhancing the existing art in super-resolution. In particular, an e ective approach for training convolutional networks is proposed, that can result in optimized and quick training of complex models. In addition, speci c deep learning architectures with novel elements are introduced that can provide reduction in the complexity of the existing solutions, and improve the super-resolution models to achieve better picture quality. Furthermore, application of super-resolution for handling compressed content, and its functionality as a compression tool are studied and investigated.COGNITUS media AI software fundin