{"url":"/task/person-identification","name":"Person Identification","slug":"person-identification","description_markdown":null,"categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":107,"papers_with_code":24,"benchmarks":3,"benchmark_tables_in_archive":3,"benchmark_tables_shown":3,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":10,"subtasks":0,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/person-identification-on-eeg-motor-movement","slug":"person-identification-on-eeg-motor-movement","dataset":"EEG Motor Movement/Imagery Dataset","dataset_url":"/dataset/eeg-motor-movement-imagery-dataset","rows_in_archive":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"SSL","paper_title":"Subject-Aware Contrastive Learning for Biosignals","paper_url":"/paper/subject-aware-contrastive-learning-for","paper_date":"2020-06-30","arxiv_id":"2007.04871","code_links":[{"title":"zacharycbrown/ssl_baselines_for_biosignal_feature_extraction","url":"https://github.com/zacharycbrown/ssl_baselines_for_biosignal_feature_extraction"}],"syntology":null}},{"leaderboard":"/sota/person-identification-on-wigesture","slug":"person-identification-on-wigesture","dataset":"WiGesture","dataset_url":"/dataset/wigesture","rows_in_archive":3,"metrics":["Accuracy (% )"],"first_row_in_archive_order":{"model":"CrossFi","paper_title":"CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network","paper_url":"/paper/crossfi-a-cross-domain-wi-fi-sensing","paper_date":"2024-08-20","arxiv_id":"2408.10919","code_links":[{"title":"RS2002/CrossFi","url":"https://github.com/RS2002/CrossFi"}],"syntology":null}},{"leaderboard":"/sota/person-identification-on-bioeye","slug":"person-identification-on-bioeye","dataset":"BioEye","dataset_url":null,"rows_in_archive":1,"metrics":["R1"],"first_row_in_archive_order":{"model":"RBFN","paper_title":"A Score-level Fusion Method for Eye Movement Biometrics","paper_url":"/paper/a-score-level-fusion-method-for-eye-movement","paper_date":"2016-01-13","arxiv_id":"1601.03333","code_links":[],"syntology":null}}],"datasets":[{"url":"/dataset/11k-hands","name":"11k Hands","full_name":"","num_papers_in_archive":82},{"url":"/dataset/iqiyi-vid","name":"iQIYI-VID","full_name":"iQIYI-VID","num_papers_in_archive":6},{"url":"/dataset/wigesture","name":"WiGesture","full_name":"Wireless Sensing Dataset for Gesture Recognition and People ID Identification with ESP32","num_papers_in_archive":5},{"url":"/dataset/eeg-motor-movement-imagery-dataset","name":"EEG Motor Movement/Imagery Dataset","full_name":"","num_papers_in_archive":4},{"url":"/dataset/nsva","name":"NSVA","full_name":"NBA dataset for Sports Video Analysis","num_papers_in_archive":2},{"url":"/dataset/wifall","name":"WiFall","full_name":"Wireless Sensing Dataset for Fall Detection, Action Recognition and People ID Identification with ESP32-S3","num_papers_in_archive":2},{"url":"/dataset/motionid-imu-all-motions-part1","name":"MotionID: IMU all motions part1","full_name":"Motion Patterns Identification","num_papers_in_archive":1},{"url":"/dataset/motionid-imu-all-motions-part2","name":"MotionID: IMU all motions part2","full_name":"Motion Patterns Identification","num_papers_in_archive":1},{"url":"/dataset/motionid-imu-all-motions-part3","name":"MotionID: IMU all motions part3","full_name":"Motion Patterns Identification","num_papers_in_archive":1},{"url":"/dataset/motionid-imu-specific-motion","name":"MotionID: IMU specific motion","full_name":"User verification","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":24,"of":24,"tagged_in_all":107,"items":[{"url":"/paper/robust-m-estimation-based-bayesian-cluster","title":"Robust M-Estimation Based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions","date":"2020-05-04","arxiv_id":"2005.01404","repositories_listed":3,"syntology":null},{"url":"/paper/weakly-supervised-discriminative-feature","title":"Weakly supervised discriminative feature learning with state information for person identification","date":"2020-02-27","arxiv_id":"2002.11939","repositories_listed":2,"syntology":null},{"url":"/paper/hribench-benchmarking-vision-language-models","title":"HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot Interaction","date":"2025-06-25","arxiv_id":"2506.20566","repositories_listed":1,"syntology":null},{"url":"/paper/knn-mmd-cross-domain-wi-fi-sensing-based-on","title":"KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment","date":"2024-12-06","arxiv_id":"2412.04783","repositories_listed":1,"syntology":null},{"url":"/paper/crossfi-a-cross-domain-wi-fi-sensing","title":"CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network","date":"2024-08-20","arxiv_id":"2408.10919","repositories_listed":1,"syntology":null},{"url":"/paper/opengait-a-comprehensive-benchmark-study-for","title":"OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality","date":"2024-05-15","arxiv_id":"2405.09138","repositories_listed":1,"syntology":null},{"url":"/paper/activity-biometrics-person-identification","title":"Activity-Biometrics: Person Identification from Daily Activities","date":"2024-03-26","arxiv_id":"2403.17360","repositories_listed":1,"syntology":null},{"url":"/paper/finding-the-missing-data-a-bert-inspired","title":"Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing","date":"2024-03-19","arxiv_id":"2403.12400","repositories_listed":1,"syntology":null},{"url":"/paper/image-based-human-re-identification-which","title":"Image-based human re-identification: Which covariates are actually (the most) important?","date":"2024-01-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/class-distribution-shifts-in-zero-shot","title":"Class Distribution Shifts in Zero-Shot Learning: Learning Robust Representations","date":"2023-11-30","arxiv_id":"2311.18575","repositories_listed":1,"syntology":null},{"url":"/paper/gaitformer-learning-gait-representations-with","title":"GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning","date":"2023-10-30","arxiv_id":"2310.19418","repositories_listed":1,"syntology":null},{"url":"/paper/nipd-a-federated-learning-person-detection","title":"NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data","date":"2023-06-28","arxiv_id":"2306.15932","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-deep-models-for-practical-gait","title":"Exploring Deep Models for Practical Gait Recognition","date":"2023-03-06","arxiv_id":"2303.03301","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":3}},{"url":"/paper/attribute-de-biased-vision-transformer-ad-vit","title":"Attribute De-biased Vision Transformer (AD-ViT) for Long-Term Person Re-identification","date":"2022-11-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/disarm-detecting-the-victims-targeted-by-1","title":"DISARM: Detecting the Victims Targeted by Harmful Memes","date":"2022-05-11","arxiv_id":"2205.05738","repositories_listed":1,"syntology":null},{"url":"/paper/long-term-person-re-identification-a","title":"DeepChange: A Large Long-Term Person Re-Identification Benchmark with Clothes Change","date":"2021-05-31","arxiv_id":"2105.14685","repositories_listed":1,"syntology":null},{"url":"/paper/3d-human-body-reshaping-with-anthropometric","title":"3D Human Body Reshaping with Anthropometric Modeling","date":"2021-04-05","arxiv_id":"2104.01762","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-multi-face-3d","title":"Weakly-Supervised Multi-Face 3D Reconstruction","date":"2021-01-06","arxiv_id":"2101.02000","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-disentanglement-of-speaker","title":"Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation","date":"2020-12-08","arxiv_id":"2012.04454","repositories_listed":1,"syntology":null},{"url":"/paper/subject-aware-contrastive-learning-for","title":"Subject-Aware Contrastive Learning for Biosignals","date":"2020-06-30","arxiv_id":"2007.04871","repositories_listed":1,"syntology":null},{"url":"/paper/ear2face-deep-biometric-modality-mapping","title":"Ear2Face: Deep Biometric Modality Mapping","date":"2020-06-02","arxiv_id":"2006.01943","repositories_listed":1,"syntology":null},{"url":"/paper/opfython-a-python-inspired-optimum-path","title":"OPFython: A Python-Inspired Optimum-Path Forest Classifier","date":"2020-01-28","arxiv_id":"2001.10420","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-based-gait-recognition-using","title":"Deep Learning-Based Gait Recognition Using Smartphones in the Wild","date":"2018-11-01","arxiv_id":"1811.00338","repositories_listed":1,"syntology":null},{"url":"/paper/storygraphs-visualizing-character","title":"StoryGraphs: Visualizing Character Interactions as a Timeline","date":"2014-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"syntology_records":1,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}