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Found 19 papers, 2 papers with code
BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images
Current state-of-the-art segmentation techniques for ocular images are critically dependent on large-scale annotated datasets, which are labor-intensive to gather and often raise privacy concerns.
A Global Modeling Approach for Load Forecasting in Distribution Networks
Efficient load forecasting is needed to ensure better observability in the distribution networks, whereas such forecasting is made possible by an increasing number of smart meter installations.
An Attack on Feature Level-based Facial Soft-biometric Privacy Enhancement
The attack is based on two observations: (1) to achieve high recognition accuracy, certain similarities between facial representations have to be retained in their privacy-enhanced versions; (2) highly similar facial representations usually originate from face images with similar soft-biometric attributes.
Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection
Due to the separation of semantically-relevant and residual information, Y-GAN is able to derive informative data representations that allow for efficient anomaly detection across a diverse set of anomaly detection tasks.
MFR 2021: Masked Face Recognition Competition
These teams successfully submitted 18 valid solutions.
High Resolution Face Editing with Masked GAN Latent Code Optimization
The proposed approach is based on an optimization procedure that directly optimizes the latent code of a pre-trained (state-of-the-art) Generative Adversarial Network (i. e., StyleGAN2) with respect to several constraints that ensure: (i) preservation of relevant image content, (ii) generation of the targeted facial attributes, and (iii) spatially--selective treatment of local image areas.
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.
Simultaneous regression and feature learning for facial landmarking
We develop two distinct approaches around the proposed gating mechanism: i) the first uses a gated multiple ridge descent (GRID) mechanism in conjunction with established (hand-crafted) HOG features for face alignment and achieves state-of-the-art landmarking performance across a wide range of facial poses, ii) the second simultaneously learns multiple-descent directions as well as binary features (SMUF) that are optimal for the alignment tasks and in addition to competitive landmarking results also ensures extremely rapid processing.
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.
The Unconstrained Ear Recognition Challenge 2019 - ArXiv Version With Appendix
The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i. e. gender and ethnicity.
Influence of segmentation on deep iris recognition performance
How important is the use of traditional segmentation methods in a deep learning setting?
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.
Training Convolutional Neural Networks with Limited Training Data for Ear Recognition in the Wild
The result of our work is the first CNN-based approach to ear recognition that is also made publicly available to the research community.
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.
The Unconstrained Ear Recognition Challenge
In this paper we present the results of the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem of person recognition from ear images captured in uncontrolled conditions.
Face Deidentification with Generative Deep Neural Networks
Our results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is highly effective.
Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks
For our technique, we formulate the problem of ear detection as a two-class segmentation problem and train a convolutional encoder-decoder network based on the SegNet architecture to distinguish between image-pixels belonging to either the ear or the non-ear class.
Ear Recognition: More Than a Survey
This paper presents an overview of the field of automatic ear recognition (from 2D images) and focuses specifically on the most recent, descriptor-based methods proposed in this area.
