{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/face-recognition/papers/5","list_of":"/task/face-recognition","task":"Face Recognition","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":5,"pages_in_order":24,"rows_per_page":100,"rows":[401,500],"of":2329,"counts":{"archive_papers_tagged":2329,"with_a_code_link":639,"where_syntology_ran_a_sample":97,"not_listed_spam_title":0,"listed":2329,"listed_where_code_ran":97,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":83,"every_run_a_failure_of_syntologys_instrument":14,"listed_with_a_run_with_no_instrument_failure":83,"listed_every_run_a_failure_of_syntologys_instrument":14,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/face-recognition","prev":"/task/face-recognition/papers/4","next":"/task/face-recognition/papers/6","papers":[{"url":"/paper/trusted-maximizers-entropy-search-for","slug":"trusted-maximizers-entropy-search-for","title":"Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization","date":"2021-07-30","arxiv_id":"2107.14465","repositories_listed":1,"syntology":null},{"url":"/paper/rank-based-verification-for-long-term-face","slug":"rank-based-verification-for-long-term-face","title":"Rank-based verification for long-term face tracking in crowded scenes","date":"2021-07-28","arxiv_id":"2107.13273","repositories_listed":1,"syntology":null},{"url":"/paper/mixfacenets-extremely-efficient-face","slug":"mixfacenets-extremely-efficient-face","title":"MixFaceNets: Extremely Efficient Face Recognition Networks","date":"2021-07-27","arxiv_id":"2107.13046","repositories_listed":1,"syntology":null},{"url":"/paper/face-evolve-a-high-performance-face","slug":"face-evolve-a-high-performance-face","title":"Face.evoLVe: A High-Performance Face Recognition Library","date":"2021-07-19","arxiv_id":"2107.08621","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/face-evolve-a-high-performance-face#ran","syntology_url":"https://syntology.ai/paper/2107.08621","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.08621"}},"official":{"repos":["ZhaoJ9014/face.evoLVe.PyTorch"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-jigsaw-learning-for-cartoon-face","slug":"graph-jigsaw-learning-for-cartoon-face","title":"Graph Jigsaw Learning for Cartoon Face Recognition","date":"2021-07-14","arxiv_id":"2107.06532","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-and-temporal-networks-for-facial","slug":"spatial-and-temporal-networks-for-facial","title":"Spatial and Temporal Networks for Facial Expression Recognition in the Wild Videos","date":"2021-07-12","arxiv_id":"2107.05160","repositories_listed":1,"syntology":null},{"url":"/paper/image-resolution-susceptibility-of-face","slug":"image-resolution-susceptibility-of-face","title":"Susceptibility to Image Resolution in Face Recognition and Trainings Strategies","date":"2021-07-08","arxiv_id":"2107.03769","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adaptation-for-person-re","slug":"domain-adaptation-for-person-re","title":"Domain adaptation for person re-identification on new unlabeled data using AlignedReID++","date":"2021-06-29","arxiv_id":"2106.15693","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-realistic-data-generation-framework","slug":"efficient-realistic-data-generation-framework","title":"Efficient Realistic Data Generation Framework leveraging Deep Learning-based Human Digitization","date":"2021-06-28","arxiv_id":"2106.15409","repositories_listed":1,"syntology":null},{"url":"/paper/attention-guided-progressive-mapping-for","slug":"attention-guided-progressive-mapping-for","title":"Attention-guided Progressive Mapping for Profile Face Recognition","date":"2021-06-27","arxiv_id":"2106.14124","repositories_listed":1,"syntology":null},{"url":"/paper/darker-than-black-box-face-reconstruction","slug":"darker-than-black-box-face-reconstruction","title":"Darker than Black-Box: Face Reconstruction from Similarity Queries","date":"2021-06-27","arxiv_id":"2106.14290","repositories_listed":1,"syntology":null},{"url":"/paper/lb-cnn-an-open-source-framework-for-fast","slug":"lb-cnn-an-open-source-framework-for-fast","title":"LB-CNN: An Open Source Framework for Fast Training of Light Binary Convolutional Neural Networks using Chainer and Cupy","date":"2021-06-25","arxiv_id":"2106.15350","repositories_listed":1,"syntology":null},{"url":"/paper/information-bottleneck-disentanglement-for","slug":"information-bottleneck-disentanglement-for","title":"Information Bottleneck Disentanglement for Identity Swapping","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/quality-agnostic-image-recognition-via","slug":"quality-agnostic-image-recognition-via","title":"Quality-Agnostic Image Recognition via Invertible Decoder","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/spherical-confidence-learning-for-face","slug":"spherical-confidence-learning-for-face","title":"Spherical Confidence Learning for Face Recognition","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/virtual-fully-connected-layer-training-a","slug":"virtual-fully-connected-layer-training-a","title":"Virtual Fully-Connected Layer: Training a Large-Scale Face Recognition Dataset With Limited Computational Resources","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/wasserstein-barycenter-for-multi-source","slug":"wasserstein-barycenter-for-multi-source","title":"Wasserstein Barycenter for Multi-Source Domain Adaptation","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/hififace-3d-shape-and-semantic-prior-guided","slug":"hififace-3d-shape-and-semantic-prior-guided","title":"HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping","date":"2021-06-18","arxiv_id":"2106.09965","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":6,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified; every one of the 6 samples that ran constructed an object rather than computing a result","sample_list":"/paper/hififace-3d-shape-and-semantic-prior-guided#ran","syntology_url":"https://syntology.ai/paper/2106.09965","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.09965"}},"official":null}},{"url":"/paper/sejong-face-database-a-multi-modal-disguise","slug":"sejong-face-database-a-multi-modal-disguise","title":"Sejong Face Database: A Multi-Modal Disguise Face Database","date":"2021-06-14","arxiv_id":"2106.07186","repositories_listed":1,"syntology":null},{"url":"/paper/attention-based-partial-face-recognition","slug":"attention-based-partial-face-recognition","title":"Attention-based Partial Face Recognition","date":"2021-06-11","arxiv_id":"2106.06415","repositories_listed":1,"syntology":null},{"url":"/paper/learning-the-precise-feature-for-cluster","slug":"learning-the-precise-feature-for-cluster","title":"Learning the Precise Feature for Cluster Assignment","date":"2021-06-11","arxiv_id":"2106.06159","repositories_listed":1,"syntology":null},{"url":"/paper/consistent-instance-false-positive-improves","slug":"consistent-instance-false-positive-improves","title":"Consistent Instance False Positive Improves Fairness in Face Recognition","date":"2021-06-10","arxiv_id":"2106.05519","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/consistent-instance-false-positive-improves#ran","syntology_url":"https://syntology.ai/paper/2106.05519","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05519"}},"official":{"repos":["Tencent/TFace"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-dataset-benchmarks-for-masked","slug":"multi-dataset-benchmarks-for-masked","title":"Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning","date":"2021-06-10","arxiv_id":"2106.05596","repositories_listed":1,"syntology":null},{"url":"/paper/progressive-scale-boundary-blackbox-attack","slug":"progressive-scale-boundary-blackbox-attack","title":"Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation","date":"2021-06-10","arxiv_id":"2106.06056","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":1,"n_honours":4,"n_violates":0,"n_no_contract":0,"n_pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 4 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/progressive-scale-boundary-blackbox-attack#ran","syntology_url":"https://syntology.ai/paper/2106.06056","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06056"}},"official":{"repos":["AI-secure/PSBA"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-use-of-automatically-generated","slug":"on-the-use-of-automatically-generated","title":"On the use of automatically generated synthetic image datasets for benchmarking face recognition","date":"2021-06-08","arxiv_id":"2106.04215","repositories_listed":1,"syntology":null},{"url":"/paper/variational-leakage-the-role-of-information","slug":"variational-leakage-the-role-of-information","title":"Variational Leakage: The Role of Information Complexity in Privacy Leakage","date":"2021-06-05","arxiv_id":"2106.02818","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-class-queue-for-large-scale-face","slug":"dynamic-class-queue-for-large-scale-face","title":"Dynamic Class Queue for Large Scale Face Recognition In the Wild","date":"2021-05-24","arxiv_id":"2105.11113","repositories_listed":1,"syntology":null},{"url":"/paper/an-efficient-training-approach-for-very-large","slug":"an-efficient-training-approach-for-very-large","title":"An Efficient Training Approach for Very Large Scale Face Recognition","date":"2021-05-21","arxiv_id":"2105.10375","repositories_listed":1,"syntology":null},{"url":"/paper/superpixel-based-domain-knowledge-infusion-in","slug":"superpixel-based-domain-knowledge-infusion-in","title":"Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification","date":"2021-05-20","arxiv_id":"2105.09448","repositories_listed":1,"syntology":null},{"url":"/paper/facial-age-estimation-using-convolutional","slug":"facial-age-estimation-using-convolutional","title":"Facial Age Estimation using Convolutional Neural Networks","date":"2021-05-14","arxiv_id":"2105.06746","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-semi-supervised-face-recognition","slug":"boosting-semi-supervised-face-recognition","title":"Boosting Semi-Supervised Face Recognition with Noise Robustness","date":"2021-05-10","arxiv_id":"2105.04431","repositories_listed":1,"syntology":null},{"url":"/paper/adv-makeup-a-new-imperceptible-and","slug":"adv-makeup-a-new-imperceptible-and","title":"Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition","date":"2021-05-07","arxiv_id":"2105.03162","repositories_listed":1,"syntology":null},{"url":"/paper/mafer-a-multi-resolution-approach-to-facial","slug":"mafer-a-multi-resolution-approach-to-facial","title":"MAFER: a Multi-resolution Approach to Facial Expression Recognition","date":"2021-05-06","arxiv_id":"2105.02481","repositories_listed":1,"syntology":null},{"url":"/paper/dual-cross-central-difference-network-for","slug":"dual-cross-central-difference-network-for","title":"Dual-Cross Central Difference Network for Face Anti-Spoofing","date":"2021-05-04","arxiv_id":"2105.01290","repositories_listed":1,"syntology":null},{"url":"/paper/eqface-a-simple-explicit-quality-network-for","slug":"eqface-a-simple-explicit-quality-network-for","title":"EQFace: A Simple Explicit Quality Network for Face Recognition","date":"2021-05-03","arxiv_id":"2105.00634","repositories_listed":1,"syntology":null},{"url":"/paper/bi-fpnfas-bi-directional-feature-pyramid","slug":"bi-fpnfas-bi-directional-feature-pyramid","title":"Bi-FPNFAS: Bi-Directional Feature Pyramid Network for Pixel-Wise Face Anti-Spoofing by Leveraging Fourier Spectra","date":"2021-04-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/meaningful-adversarial-stickers-for-face","slug":"meaningful-adversarial-stickers-for-face","title":"Adversarial Sticker: A Stealthy Attack Method in the Physical World","date":"2021-04-14","arxiv_id":"2104.06728","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/meaningful-adversarial-stickers-for-face#ran","syntology_url":"https://syntology.ai/paper/2104.06728","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.06728"}},"official":{"repos":["jinyugy21/adv-stickers_rhde"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/facesec-a-fine-grained-robustness-evaluation","slug":"facesec-a-fine-grained-robustness-evaluation","title":"FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems","date":"2021-04-08","arxiv_id":"2104.04107","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/facesec-a-fine-grained-robustness-evaluation#ran","syntology_url":"https://syntology.ai/paper/2104.04107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.04107"}},"official":{"repos":["KnowledgeDiscovery/FaceSec"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sreds-a-dichromatic-separation-based-measure","slug":"sreds-a-dichromatic-separation-based-measure","title":"SREDS: A dichromatic separation based measure of skin color","date":"2021-04-07","arxiv_id":"2104.02926","repositories_listed":1,"syntology":null},{"url":"/paper/teacher-student-adversarial-depth","slug":"teacher-student-adversarial-depth","title":"Teacher-Student Adversarial Depth Hallucination to Improve Face Recognition","date":"2021-04-06","arxiv_id":"2104.02424","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-robustness-of-on-line-learning","slug":"enhancing-robustness-of-on-line-learning","title":"Enhancing Robustness of On-line Learning Models on Highly Noisy Data","date":"2021-03-19","arxiv_id":"2103.10824","repositories_listed":1,"syntology":null},{"url":"/paper/larnet-lie-algebra-residual-network-for","slug":"larnet-lie-algebra-residual-network-for","title":"LARNet: Lie Algebra Residual Network for Face Recognition","date":"2021-03-15","arxiv_id":"2103.08147","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/larnet-lie-algebra-residual-network-for#ran","syntology_url":"https://syntology.ai/paper/2103.08147","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.08147"}},"official":null}},{"url":"/paper/sdd-fiqa-unsupervised-face-image-quality","slug":"sdd-fiqa-unsupervised-face-image-quality","title":"SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance","date":"2021-03-10","arxiv_id":"2103.05977","repositories_listed":1,"syntology":null},{"url":"/paper/when-face-recognition-meets-occlusion-a-new","slug":"when-face-recognition-meets-occlusion-a-new","title":"When Face Recognition Meets Occlusion: A New Benchmark","date":"2021-03-04","arxiv_id":"2103.02805","repositories_listed":1,"syntology":null},{"url":"/paper/k-face-a-large-scale-kist-face-database-in","slug":"k-face-a-large-scale-kist-face-database-in","title":"K-FACE: A Large-Scale KIST Face Database in Consideration with Unconstrained Environments","date":"2021-03-03","arxiv_id":"2103.02211","repositories_listed":1,"syntology":null},{"url":"/paper/unmasking-face-embeddings-by-self-restrained","slug":"unmasking-face-embeddings-by-self-restrained","title":"Self-restrained Triplet Loss for Accurate Masked Face Recognition","date":"2021-03-02","arxiv_id":"2103.01716","repositories_listed":1,"syntology":null},{"url":"/paper/when-age-invariant-face-recognition-meets","slug":"when-age-invariant-face-recognition-meets","title":"When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework","date":"2021-03-02","arxiv_id":"2103.01520","repositories_listed":1,"syntology":null},{"url":"/paper/a-3d-model-based-approach-for-fitting-masks","slug":"a-3d-model-based-approach-for-fitting-masks","title":"A 3D model-based approach for fitting masks to faces in the wild","date":"2021-03-01","arxiv_id":"2103.00803","repositories_listed":1,"syntology":null},{"url":"/paper/robust-sleepnets","slug":"robust-sleepnets","title":"Robust SleepNets","date":"2021-02-24","arxiv_id":"2102.12555","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-and-mitigating-accuracy-1","slug":"understanding-and-mitigating-accuracy-1","title":"Understanding and Mitigating Accuracy Disparity in Regression","date":"2021-02-24","arxiv_id":"2102.12013","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/understanding-and-mitigating-accuracy-1#ran","syntology_url":"https://syntology.ai/paper/2102.12013","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.12013"}},"official":{"repos":["JFChi/Understanding-and-Mitigating-Accuracy-Disparity-in-Regression"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/reliable-probabilistic-face-embeddings-in-the","slug":"reliable-probabilistic-face-embeddings-in-the","title":"Fast and Reliable Probabilistic Face Embeddings in the Wild","date":"2021-02-08","arxiv_id":"2102.04075","repositories_listed":1,"syntology":null},{"url":"/paper/single-image-super-resolution-using-residual","slug":"single-image-super-resolution-using-residual","title":"Single Image Super-Resolution using Residual Channel Attention Network","date":"2021-02-08","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/binarycop-binary-neural-network-based-covid","slug":"binarycop-binary-neural-network-based-covid","title":"BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices","date":"2021-02-06","arxiv_id":"2102.03456","repositories_listed":1,"syntology":null},{"url":"/paper/multiface-a-generic-training-mechanism-for","slug":"multiface-a-generic-training-mechanism-for","title":"MultiFace: A Generic Training Mechanism for Boosting Face Recognition Performance","date":"2021-01-25","arxiv_id":"2101.09899","repositories_listed":1,"syntology":null},{"url":"/paper/non-parametric-adaptive-network-pruning","slug":"non-parametric-adaptive-network-pruning","title":"Network Pruning using Adaptive Exemplar Filters","date":"2021-01-20","arxiv_id":"2101.07985","repositories_listed":1,"syntology":null},{"url":"/paper/analysis-and-evaluation-of-deep-learning","slug":"analysis-and-evaluation-of-deep-learning","title":"Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face Recognition","date":"2021-01-19","arxiv_id":"2101.10845","repositories_listed":1,"syntology":null},{"url":"/paper/unlearnable-examples-making-personal-data-1","slug":"unlearnable-examples-making-personal-data-1","title":"Unlearnable Examples: Making Personal Data Unexploitable","date":"2021-01-13","arxiv_id":"2101.04898","repositories_listed":1,"syntology":null},{"url":"/paper/depth-as-attention-for-face-representation","slug":"depth-as-attention-for-face-representation","title":"Depth as Attention for Face Representation Learning","date":"2021-01-03","arxiv_id":"2101.00652","repositories_listed":1,"syntology":null},{"url":"/paper/devi-open-source-human-robot-interface-for","slug":"devi-open-source-human-robot-interface-for","title":"DEVI: Open-source Human-Robot Interface for Interactive Receptionist Systems","date":"2021-01-02","arxiv_id":"2101.00479","repositories_listed":1,"syntology":null},{"url":"/paper/incremental-embedding-learning-via-zero-shot","slug":"incremental-embedding-learning-via-zero-shot","title":"Incremental Embedding Learning via Zero-Shot Translation","date":"2020-12-31","arxiv_id":"2012.15497","repositories_listed":1,"syntology":null},{"url":"/paper/ostec-one-shot-texture-completion","slug":"ostec-one-shot-texture-completion","title":"OSTeC: One-Shot Texture Completion","date":"2020-12-30","arxiv_id":"2012.15370","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-threshold-for-better-performance-of","slug":"adaptive-threshold-for-better-performance-of","title":"Adaptive Threshold for Online Object Recognition and Re-identification Tasks","date":"2020-12-28","arxiv_id":"2012.14305","repositories_listed":1,"syntology":null},{"url":"/paper/foggysight-a-scheme-for-facial-lookup-privacy","slug":"foggysight-a-scheme-for-facial-lookup-privacy","title":"FoggySight: A Scheme for Facial Lookup Privacy","date":"2020-12-15","arxiv_id":"2012.08588","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-review-of-adversarial-defenses","slug":"an-empirical-review-of-adversarial-defenses","title":"An Empirical Review of Adversarial Defenses","date":"2020-12-10","arxiv_id":"2012.06332","repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-fairness-of-deep-generative","slug":"improving-the-fairness-of-deep-generative","title":"Improving the Fairness of Deep Generative Models without Retraining","date":"2020-12-09","arxiv_id":"2012.04842","repositories_listed":1,"syntology":null},{"url":"/paper/vulnerability-analysis-of-face-morphing","slug":"vulnerability-analysis-of-face-morphing","title":"Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks","date":"2020-12-09","arxiv_id":"2012.05344","repositories_listed":1,"syntology":null},{"url":"/paper/maad-face-a-massively-annotated-attribute","slug":"maad-face-a-massively-annotated-attribute","title":"MAAD-Face: A Massively Annotated Attribute Dataset for Face Images","date":"2020-12-02","arxiv_id":"2012.01030","repositories_listed":1,"syntology":null},{"url":"/paper/sar-net-a-end-to-end-deep-speech-accent","slug":"sar-net-a-end-to-end-deep-speech-accent","title":"Deep Discriminative Feature Learning for Accent Recognition","date":"2020-11-25","arxiv_id":"2011.12461","repositories_listed":1,"syntology":null},{"url":"/paper/building-3d-morphable-models-from-a-single","slug":"building-3d-morphable-models-from-a-single","title":"Building 3D Morphable Models from a Single Scan","date":"2020-11-24","arxiv_id":"2011.12440","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-pixel-wise-supervision-for-face","slug":"revisiting-pixel-wise-supervision-for-face","title":"Revisiting Pixel-Wise Supervision for Face Anti-Spoofing","date":"2020-11-24","arxiv_id":"2011.12032","repositories_listed":1,"syntology":null},{"url":"/paper/lightface-a-hybrid-deep-face-recognition","slug":"lightface-a-hybrid-deep-face-recognition","title":"LightFace: A Hybrid Deep Face Recognition Framework","date":"2020-11-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/on-the-effectiveness-of-vision-transformers","slug":"on-the-effectiveness-of-vision-transformers","title":"On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing","date":"2020-11-16","arxiv_id":"2011.08019","repositories_listed":1,"syntology":null},{"url":"/paper/low-cost-enhanced-security-face-recognition","slug":"low-cost-enhanced-security-face-recognition","title":"Low cost enhanced security face recognition with stereo cameras","date":"2020-11-04","arxiv_id":"2011.02222","repositories_listed":1,"syntology":null},{"url":"/paper/loss-rescaling-vqa-revisiting-language-prior","slug":"loss-rescaling-vqa-revisiting-language-prior","title":"Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance View","date":"2020-10-30","arxiv_id":"2010.16010","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/loss-rescaling-vqa-revisiting-language-prior#ran","syntology_url":"https://syntology.ai/paper/2010.16010","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.16010"}},"official":{"repos":["guoyang9/class-imbalance-VQA"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/image-representations-learned-with","slug":"image-representations-learned-with","title":"Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases","date":"2020-10-28","arxiv_id":"2010.15052","repositories_listed":1,"syntology":null},{"url":"/paper/nested-grassmanns-for-dimensionality","slug":"nested-grassmanns-for-dimensionality","title":"Nested Grassmannians for Dimensionality Reduction with Applications","date":"2020-10-27","arxiv_id":"2010.14589","repositories_listed":1,"syntology":null},{"url":"/paper/perception-for-autonomous-systems-paz","slug":"perception-for-autonomous-systems-paz","title":"Perception for Autonomous Systems (PAZ)","date":"2020-10-27","arxiv_id":"2010.14541","repositories_listed":1,"syntology":null},{"url":"/paper/ipu-net-multi-scale-identity-preserved-u-net","slug":"ipu-net-multi-scale-identity-preserved-u-net","title":"Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face Recognition","date":"2020-10-23","arxiv_id":"2010.12249","repositories_listed":1,"syntology":null},{"url":"/paper/face-hallucination-using-split-attention-in","slug":"face-hallucination-using-split-attention-in","title":"Face Hallucination via Split-Attention in Split-Attention Network","date":"2020-10-22","arxiv_id":"2010.11575","repositories_listed":1,"syntology":null},{"url":"/paper/long-term-face-tracking-for-crowded-video","slug":"long-term-face-tracking-for-crowded-video","title":"Long-Term Face Tracking for Crowded Video-Surveillance Scenarios","date":"2020-10-17","arxiv_id":"2010.08675","repositories_listed":1,"syntology":null},{"url":"/paper/self-attention-aggregation-network-for-video","slug":"self-attention-aggregation-network-for-video","title":"Self-attention aggregation network for video face representation and recognition","date":"2020-10-11","arxiv_id":"2010.05340","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-face-recognition-using-convoluted","slug":"real-time-face-recognition-using-convoluted","title":"Real Time Face Recognition Using Convoluted Neural Networks","date":"2020-10-09","arxiv_id":"2010.04517","repositories_listed":1,"syntology":null},{"url":"/paper/are-adaptive-face-recognition-systems-still","slug":"are-adaptive-face-recognition-systems-still","title":"Are Adaptive Face Recognition Systems still Necessary? Experiments on the APE Dataset","date":"2020-10-08","arxiv_id":"2010.04072","repositories_listed":1,"syntology":null},{"url":"/paper/masked-face-recognition-with-latent-part","slug":"masked-face-recognition-with-latent-part","title":"Masked Face Recognition with Latent Part Detection","date":"2020-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-from-limited","slug":"representation-learning-from-limited","title":"Representation Learning from Limited Educational Data with Crowdsourced Labels","date":"2020-09-23","arxiv_id":"2009.11222","repositories_listed":1,"syntology":null},{"url":"/paper/dvg-face-dual-variational-generation-for","slug":"dvg-face-dual-variational-generation-for","title":"DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition","date":"2020-09-20","arxiv_id":"2009.09399","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/dvg-face-dual-variational-generation-for#ran","syntology_url":"https://syntology.ai/paper/2009.09399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.09399"}},"official":null}},{"url":"/paper/cross-domain-identification-for-thermal-to","slug":"cross-domain-identification-for-thermal-to","title":"Cross-Domain Identification for Thermal-to-Visible Face Recognition","date":"2020-08-19","arxiv_id":"2008.08473","repositories_listed":1,"syntology":null},{"url":"/paper/broadface-looking-at-tens-of-thousands-of","slug":"broadface-looking-at-tens-of-thousands-of","title":"BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition","date":"2020-08-15","arxiv_id":"2008.06674","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/broadface-looking-at-tens-of-thousands-of#ran","syntology_url":"https://syntology.ai/paper/2008.06674","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.06674"}},"official":null}},{"url":"/paper/explainable-face-recognition","slug":"explainable-face-recognition","title":"Explainable Face Recognition","date":"2020-08-03","arxiv_id":"2008.00916","repositories_listed":1,"syntology":null},{"url":"/paper/deep-transferring-quantization","slug":"deep-transferring-quantization","title":"Deep Transferring Quantization","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/semanticadv-generating-adversarial-examples-1","slug":"semanticadv-generating-adversarial-examples-1","title":"SemanticAdv: Generating Adversarial Examples via Attribute-conditioned Image Editing","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/label-leaks-membership-inference-attack-with","slug":"label-leaks-membership-inference-attack-with","title":"Membership Leakage in Label-Only Exposures","date":"2020-07-30","arxiv_id":"2007.15528","repositories_listed":1,"syntology":null},{"url":"/paper/black-box-face-recovery-from-identity","slug":"black-box-face-recovery-from-identity","title":"Black-Box Face Recovery from Identity Features","date":"2020-07-27","arxiv_id":"2007.13635","repositories_listed":1,"syntology":null},{"url":"/paper/socrates-towards-a-unified-platform-for","slug":"socrates-towards-a-unified-platform-for","title":"SOCRATES: Towards a Unified Platform for Neural Network Analysis","date":"2020-07-22","arxiv_id":"2007.11206","repositories_listed":1,"syntology":null},{"url":"/paper/loss-function-search-for-face-recognition","slug":"loss-function-search-for-face-recognition","title":"Loss Function Search for Face Recognition","date":"2020-07-10","arxiv_id":"2007.06542","repositories_listed":1,"syntology":null},{"url":"/paper/confoc-content-focus-protection-against","slug":"confoc-content-focus-protection-against","title":"ConFoc: Content-Focus Protection Against Trojan Attacks on Neural Networks","date":"2020-07-01","arxiv_id":"2007.00711","repositories_listed":1,"syntology":null},{"url":"/paper/roweisposes-including-eigenposes-supervised","slug":"roweisposes-including-eigenposes-supervised","title":"Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition","date":"2020-06-28","arxiv_id":"2006.15736","repositories_listed":1,"syntology":null},{"url":"/paper/achieving-better-kinship-recognition-through","slug":"achieving-better-kinship-recognition-through","title":"Achieving Better Kinship Recognition Through Better Baseline","date":"2020-06-21","arxiv_id":"2006.11739","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-through-robustness-investigating","slug":"fairness-through-robustness-investigating","title":"Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning","date":"2020-06-17","arxiv_id":"2006.12621","repositories_listed":1,"syntology":null},{"url":"/paper/a2-link-recognizing-disguised-faces-via","slug":"a2-link-recognizing-disguised-faces-via","title":"A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain Knowledge","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"f13b9b88e881cddfd4b45a193cbba94e38ac8b93182d648572228230e88889ec","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}