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Found 16 papers, 8 papers with code
Adversarial Watermarking for Face Recognition
Watermarking is an essential technique for embedding an identifier (i. e., watermark message) within digital images to assert ownership and monitor unauthorized alterations.
Data Pruning via Separability, Integrity, and Model Uncertainty-Aware Importance Sampling
This paper improves upon existing data pruning methods for image classification by introducing a novel pruning metric and pruning procedure based on importance sampling.
On Missing Scores in Evolving Multibiometric Systems
We show that the application of various score imputation methods along with simple sum fusion can improve recognition accuracy, even when the proportion of missing scores increases to 90%.
Open-Set Biometrics: Beyond Good Closed-Set Models
Biometric recognition has primarily addressed closed-set identification, assuming all probe subjects are in the gallery.
Hide and Seek: How Does Watermarking Impact Face Recognition?
The recent progress in generative models has revolutionized the synthesis of highly realistic images, including face images.
KeyPoint Relative Position Encoding for Face Recognition
By anchoring the significance of pixels around keypoints, the model can more effectively retain spatial relationships, even when those relationships are disrupted by affine transformations.
Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification
Long-Term Person Re-Identification (LT-ReID) has become increasingly crucial in computer vision and biometrics.
Ranked #2 on
Person Re-Identification
on CCVID
FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude
Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance.
DCFace: Synthetic Face Generation with Dual Condition Diffusion Model
Our novel Patch-wise style extractor and Time-step dependent ID loss enables DCFace to consistently produce face images of the same subject under different styles with precise control.
Cluster and Aggregate: Face Recognition with Large Probe Set
Advances in attention and recurrent modules have led to feature fusion that can model the relationship among the images in the input set.
Ranked #1 on
Face Verification
on IJB-B
(TAR @ FAR=0.001 metric)
Multi-domain Learning for Updating Face Anti-spoofing Models
In this work, we study multi-domain learning for face anti-spoofing(MD-FAS), where a pre-trained FAS model needs to be updated to perform equally well on both source and target domains while only using target domain data for updating.
Controllable and Guided Face Synthesis for Unconstrained Face Recognition
To address this problem, we propose a controllable face synthesis model (CFSM) that can mimic the distribution of target datasets in a style latent space.
Ranked #1 on
Face Verification
on IJB-S
Towards the Memorization Effect of Neural Networks in Adversarial Training
In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN's accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN's performance on typical samples.
On the Detection of Digital Face Manipulation
Instead of simply using multi-task learning to simultaneously detect manipulated images and predict the manipulated mask (regions), we propose to utilize an attention mechanism to process and improve the feature maps for the classification task.
Face Recognition: Primates in the Wild
We present a new method of primate face recognition, and evaluate this method on several endangered primates, including golden monkeys, lemurs, and chimpanzees.
Similarity Learning via Adaptive Regression and Its Application to Image Retrieval
The similarity between pairs of images can be measured by the distances between their high dimensional representations, and the problem of learning the appropriate similarity is often addressed by distance metric learning.
