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Found 45 papers, 16 papers with code
Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data
In order to promote the proposal of novel Generative AI methods and synthetic data, and investigate the application of synthetic data to better train face recognition systems, we introduce the 2nd FRCSyn-onGoing challenge, based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024.
DiCTI: Diffusion-based Clothing Designer via Text-guided Input
Recent developments in deep generative models have opened up a wide range of opportunities for image synthesis, leading to significant changes in various creative fields, including the fashion industry.
AdaDistill: Adaptive Knowledge Distillation for Deep Face Recognition
Knowledge distillation (KD) aims at improving the performance of a compact student model by distilling the knowledge from a high-performing teacher model.
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Synthetic data is gaining increasing relevance for training machine learning models.
AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge Distillation
To address this problem, we present in this paper a novel knowledge distillation approach, termed AI-KD that can extend on any existing FIQA technique, improving its robustness to alignment variations and, in turn, performance with different alignment procedures.
eDifFIQA: Towards Efficient Face Image Quality Assessment Based On Denoising Diffusion Probabilistic Models
For this reason, Face Image Quality Assessment (FIQA) techniques are often used by FR systems, to provide quality estimates of captured face samples.
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.
On the Vulnerability of DeepFake Detectors to Attacks Generated by Denoising Diffusion Models
The detection of malicious deepfakes is a constantly evolving problem that requires continuous monitoring of detectors to ensure they can detect image manipulations generated by the latest emerging models.
Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models
Existing MAD techniques predominantly rely on discriminative models that learn from examples of bona fide and morphed images and, as a result, often exhibit sub-optimal generalization performance when confronted with unknown types of morphing attacks.
Beyond Detection: Visual Realism Assessment of Deepfakes
In the era of rapid digitalization and artificial intelligence advancements, the development of DeepFake technology has posed significant security and privacy concerns.
Optimization-Based Improvement of Face Image Quality Assessment Techniques
To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA) techniques try to infer sample-quality information from the input face images that can aid with the recognition process.
DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models
In this paper, we present a powerful new FIQA approach, named DifFIQA, which relies on denoising diffusion probabilistic models (DDPM) and ensures highly competitive results.
FICE: Text-Conditioned Fashion Image Editing With Guided GAN Inversion
We address these constraints by proposing a novel text-conditioned editing model, called FICE (Fashion Image CLIP Editing), capable of handling a wide variety of diverse text descriptions to guide the editing procedure.
Body Segmentation Using Multi-task Learning
Body segmentation is an important step in many computer vision problems involving human images and one of the key components that affects the performance of all downstream tasks.
C-VTON: Context-Driven Image-Based Virtual Try-On Network
At the core of the C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns the target clothing with the pose of the person in the input images, and (ii) a powerful image generator that utilizes various types of contextual information when synthesizing the final try-on result.
Ranked #6 on
Virtual Try-on
on VITON
FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration
In this paper, we propose a novel approach to face image quality assessment, called FaceQAN, that is based on adversarial examples and relies on the analysis of adversarial noise which can be calculated with any FR model learned by using some form of gradient descent.
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.
Assessing Bias in Face Image Quality Assessment
Although modern face recognition systems are known to perform well, several studies have found that such systems often exhibit problems with demographic bias.
PrivacyProber: Assessment and Detection of Soft-Biometric Privacy-Enhancing Techniques
Soft-biometric privacy-enhancing techniques represent machine learning methods that aim to: (i) mitigate privacy concerns associated with face recognition technology by suppressing selected soft-biometric attributes in facial images (e. g., gender, age, ethnicity) and (ii) make unsolicited extraction of sensitive personal information infeasible.
A Survey on Computer Vision based Human Analysis in the COVID-19 Era
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals.
GlassesGAN: Eyewear Personalization using Synthetic Appearance Discovery and Targeted Subspace Modeling
To facilitate the editing process with GlassesGAN, we propose a Targeted Subspace Modelling (TSM) procedure that, based on a novel mechanism for (synthetic) appearance discovery in the latent space of a pre-trained GAN generator, constructs an eyeglasses-specific (latent) subspace that the editing framework can utilize.
Hierarchical Superquadric Decomposition with Implicit Space Separation
We introduce a new method to reconstruct 3D objects using a set of volumetric primitives, i. e., superquadrics.
Iterative Optimization of Pseudo Ground-Truth Face Image Quality Labels
At ten iterations, the approach seems to perform the best, consistently outperforming the base quality scores of the three FIQA methods, chosen for the experiments.
SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data
The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries.
Face Morphing Attack Detection Using Privacy-Aware Training Data
Images of morphed faces pose a serious threat to face recognition--based security systems, as they can be used to illegally verify the identity of multiple people with a single morphed image.
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 Facial Soft-biometric Privacy Enhancement
Future works on privacy-enhancing face recognition are encouraged to include the proposed attack in evaluations on the privacy protection.
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.
