SOTAVerified

Person Identification

Papers

Showing 2650 of 107 papers

TitleStatusHype
Activity-Biometrics: Person Identification from Daily ActivitiesCode0
From Synthetic to Real: Unveiling the Power of Synthetic Data for Video Person Re-ID0
Image-based human re-identification: Which covariates are actually (the most) important?Code0
ESTformer: Transformer Utilizing Spatiotemporal Dependencies for Electroencaphalogram Super-resolution0
Class Distribution Shifts in Zero-Shot Learning: Learning Robust RepresentationsCode0
ShARc: Shape and Appearance Recognition for Person Identification In-the-wild0
Learning to Simplify Spatial-Temporal Graphs in Gait Analysis0
Human Gait Recognition using Deep Learning: A Comprehensive Review0
GaitPT: Skeletons Are All You Need For Gait Recognition0
Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using Wearables0
Towards Fair Face Verification: An In-depth Analysis of Demographic Biases0
Gait Data Augmentation using Physics-Based Biomechanical Simulation0
NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID DataCode0
Recognizing People by Body Shape Using Deep Networks of Images and Words0
Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models0
Multi-Channel Time-Series Person and Soft-Biometric Identification0
Exploring Deep Models for Practical Gait RecognitionCode0
Attribute De-biased Vision Transformer (AD-ViT) for Long-Term Person Re-identificationCode0
Online Boosting-Based Target Identification among Similar Appearance for Person-Following Robots0
Two-headed eye-segmentation approach for biometric identification0
Robust Person Identification: A WiFi Vision-based Approach0
Artificial Image Tampering Distorts Spatial Distribution of Texture Landmarks and Quality Characteristics0
Gait Cycle Reconstruction and Human Identification from Occluded Sequences0
Fused Deep Neural Network based Transfer Learning in Occluded Face Classification and Person re-Identification0
DISARM: Detecting the Victims Targeted by Harmful MemesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SSLAccuracy0.89Unverified
2Subject-invariant SSL Embedding & Linear ClassifierAccuracy0.73Unverified
3Subject-specific SSLAccuracy0.68Unverified
#ModelMetricClaimedVerifiedStatus
1CrossFiAccuracy (% )99.97Unverified
2CSI-BERT2Accuracy (% )99.73Unverified
3CSI-BERTAccuracy (% )93.94Unverified
#ModelMetricClaimedVerifiedStatus
1RBFNR198.69Unverified