Images taken under low-light conditions tend to suffer from poor visibility, which can decrease image quality and even reduce the performance of the downstream tasks.
Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks.
First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart.
Experiments results demonstrate that our RNAN achieves the comparable results with state-of-the-art methods in terms of both quantitative metrics and visual quality, however, with simplified network architecture.
We demonstrate that our two-stream architecture is robust to adversarial examples built by currently known attacking algorithms.
Recent deep learning based face recognition methods have achieved great performance, but it still remains challenging to recognize very low-resolution query face like 28x28 pixels when CCTV camera is far from the captured subject.
3D face reconstruction from a single 2D image is a challenging problem with broad applications.
Ranked #7 on Face Alignment on AFLW2000-3D
In this paper, we study the challenging unconstrained set-based face recognition problem where each subject face is instantiated by a set of media (images and videos) instead of a single image.
We propose a CNN framework using sparsely labeled data from the target domain to learn features that are invariant across domains for face anti-spoofing.
Spatio-temporal information is very important to capture the discriminative cues between genuine and fake faces from video sequences.
Face anti-spoofing (a. k. a presentation attack detection) has drawn growing attention due to the high-security demand in face authentication systems.
Ranked #2 on Face Anti-Spoofing on MSU-MFSD
Previous approaches for scene text detection usually rely on manually defined sliding windows.
Ranked #1 on Scene Text Detection on COCO-Text