Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild

Majority of the face recognition algorithms use query faces captured from uncontrolled, in the wild, environment. Often caused by the cameras limited capabilities, it is common for these captured facial images to be blurred or low resolution. Super resolution algorithms are therefore crucial in improving the resolution of such images especially when the image size is small requiring enlargement. This paper aims to demonstrate the effect of one of the state-of-the-art algorithms in the field of image super resolution. To demonstrate the functionality of the algorithm, various before and after 3D face alignment cases are provided using the images from the Labeled Faces in the Wild (lfw). Resulting images are subject to testing on a closed set face recognition protocol using unsupervised algorithms with high dimension extracted features. The inclusion of super resolution algorithm resulted in significant improved recognition rate over recently reported results obtained from unsupervised algorithms

Face Restoration via Plug-and-Play 3D Facial Priors

State-of-the-art face restoration methods employ deep convolutional neural networks (CNNs) to learn a mapping between degraded and sharp facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and only deal with task-specific face restoration (e.g.,face super-resolution or deblurring). In this paper, we propose cross-tasks and cross-models plug-and-play 3D facial priors to explicitly embed the network with the sharp facial structures for general face restoration tasks. Our 3D priors are the first to explore 3D morphable knowledge based on the fusion of parametric descriptions of face attributes (e.g., identity, facial expression, texture, illumination, and face pose). Furthermore, the priors can easily be incorporated into any network and are very efficient in improving the performance and accelerating the convergence speed. Firstly, a 3D face rendering branch is set up to obtain 3D priors of salient facial structures and identity knowledge. Secondly, for better exploiting this hierarchical information (i.e., intensity similarity, 3D facial structure, and identity content), a spatial attention module is designed for image restoration problems. Extensive face restoration experiments including face super-resolution and deblurring demonstrate that the proposed 3D priors achieve superior face restoration results over the state-of-the-art algorithm

Super Resolution and 3D Alignment Effects on Unsupervised Face Recognition in the Wild

Majority of face recognition algorithms use query faces captured from uncontrolled, in the wild, environment. It is common for these captured facial images to be blurred or low resolution. Super resolution algorithms are therefore crucial in improving the resolution of such images especially when the image size is small requiring enlargement. This paper aims to demonstrate the effect of one of the state-of-the-art algorithms in the field of image super resolution. To demonstrate the functionality of the algorithm, various before and after 3D face alignment cases are provided using the images from the Labeled Faces in the Wild (lfw). Resulting images are subjected for testing on a closed set face recognition protocol using unsupervised algorithms with high dimension extracted features

Super-resolution of faces using texture mapping on a generic 3D model

This paper proposes a novel face texture mapping framework to transform faces with different poses into a unique texture map. Under this framework, texture mapping can be realized by utilizing a generic 3D face model, standard Haar-like feature based detector, active appearance model and pose estimation algorithm. By this texture map, correspondence of every pixel at the face across multiple distinct input images can then be established, which enables super-resolution algorithms to be applied directly on registered texture map to render high resolution faces. This paper details the proposed framework, and illustrates how the proposed super-resolution algorithm works with the help of weighted average and median filters. Convincing experimental results are also presented to validate the effectiveness of the proposed framework and superresolution algorithm. © 2009 IEEE.published_or_final_versio

Super-resolution of faces using texture mapping on a generic 3D model

This paper proposes a novel face texture mapping framework to transform faces with different poses into a unique texture map. Under this framework, texture mapping can be realized by utilizing a generic 3D face model, standard Haar-like feature based detector, active appearance model and pose estimation algorithm. By this texture map, correspondence of every pixel at the face across multiple distinct input images can then be established, which enables super-resolution algorithms to be applied directly on registered texture map to render high resolution faces. This paper details the proposed framework, and illustrates how the proposed super-resolution algorithm works with the help of weighted average and median filters. Convincing experimental results are also presented to validate the effectiveness of the proposed framework and superresolution algorithm. © 2009 IEEE.published_or_final_versio

SuperCLEM: An accessible correlative light and electron microscopy approach for investigation of neurons and glia in vitro

The rapid evolution of super-resolution light microscopy has narrowed the gap between light and electron microscopy, allowing the imaging of molecules and cellular structures at high resolution within their normal cellular and tissue context. Multimodal imaging approaches such as correlative light electron microscopy (CLEM) combine these techniques to create a tool with unique imaging capacity. However, these approaches are typically reserved for specialists, and their application to the analysis of neural tissue is challenging. Here we present SuperCLEM, a relatively simple approach that combines super-resolution fluorescence light microscopy (FLM), 3D electron microscopy (3D-EM) and rendering into 3D models. We demonstrate our workflow using neuron-glia cultures from which we first acquire high-resolution fluorescent light images of myelinated axons. After resin embedding and re-identification of the region of interest, serially aligned EM sections are acquired and imaged using a serial block face scanning electron microscope (SBF-SEM). The FLM and 3D-EM data sets are then combined to render 3D models of the myelinated axons. Thus, the SuperCLEM imaging pipeline is a useful new tool for researchers pursuing similar questions in neuronal, as well as other complex tissue culture systems

Ultrastructural visualization of 3D chromatin folding using volume electron microscopy and DNA in situ hybridization.

The human genome is extensively folded into 3-dimensional organization. However, the detailed 3D chromatin folding structures have not been fully visualized due to the lack of robust and ultra-resolution imaging capability. Here, we report the development of an electron microscopy method that combines serial block-face scanning electron microscopy with in situ hybridization (3D-EMISH) to visualize 3D chromatin folding at targeted genomic regions with ultra-resolution (5 × 5 × 30 nm in xyz dimensions) that is superior to the current super-resolution by fluorescence light microscopy. We apply 3D-EMISH to human lymphoblastoid cells at a 1.7 Mb segment of the genome and visualize a large number of distinctive 3D chromatin folding structures in ultra-resolution. We further quantitatively characterize the reconstituted chromatin folding structures by identifying sub-domains, and uncover a high level heterogeneity of chromatin folding ultrastructures in individual nuclei, suggestive of extensive dynamic fluidity in 3D chromatin states