Search Results for author:
Found 9 papers, 2 papers with code
EFaR 2023: Efficient Face Recognition Competition
To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size.
Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition
In this paper, we aim to address the large domain gap between high-resolution face images, e. g., from professional portrait photography, and low-quality surveillance images, e. g., from security cameras.
MFR 2021: Masked Face Recognition Competition
These teams successfully submitted 18 valid solutions.
Segmentation and Recovery of Superquadric Models using Convolutional Neural Networks
In this paper we address the problem of representing 3D visual data with parameterized volumetric shape primitives.
Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study
It has been a longstanding goal in computer vision to describe the 3D physical space in terms of parameterized volumetric models that would allow autonomous machines to understand and interact with their surroundings.
Face Hallucination Revisited: An Exploratory Study on Dataset Bias
This down-sampling (or degradation) procedure not only defines the characteristics of the LR training data, but also determines the type of image degradations the learned FH models are eventually able to handle.
Face hallucination using cascaded super-resolution and identity priors
In this paper we address the problem of hallucinating high-resolution facial images from unaligned low-resolution inputs at high magnification factors.
UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition
Advances in image restoration and enhancement techniques have led to discussion about how such algorithmscan be applied as a pre-processing step to improve automatic visual recognition.
Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations
However, studies systematically exploring the strengths and weaknesses of existing deep models for face recognition are still relatively scarce in the literature.
