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Found 24 papers, 7 papers with code
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.
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.
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.
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.
SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes
Modern deepfake detectors have achieved encouraging results, when training and test images are drawn from the same data collection.
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.
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.
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.
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?
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.
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.
Fine Hand Segmentation using Convolutional Neural Networks
We propose a method for extracting very accurate masks of hands in egocentric views.
