{"url":"/sota/face-verification-on-calfw","task":{"name":"Face Verification","url":"/task/face-verification","note":null},"dataset":{"name":"CALFW","url":"/dataset/calfw"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Face Verification** is a machine learning task in computer vision that involves determining whether two facial images belong to the same person or not. The task involves extracting features from the facial images, such as the shape and texture of the face, and then using these features to compare and verify the similarity between the images.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Pose-Robust Face Recognition via Deep Residual Equivariant Mapping](https://arxiv.org/pdf/1803.00839v1.pdf) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Accuracy"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher"}},"counts":{"rows":2,"rows_with_code":1,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"DiscFace","metrics":{"Accuracy":"96.15"},"uses_additional_data":false,"paper_date":"2020-11-30","paper":"/paper/discface-minimum-discrepancy-learning-for","paper_url":"https://openaccess.thecvf.com/content/ACCV2020/html/Kim_DiscFace_Minimum_Discrepancy_Learning_for_Deep_Face_Recognition_ACCV_2020_paper.html","paper_title":"DiscFace: Minimum Discrepancy Learning for Deep Face Recognition","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"SFace","metrics":{"Accuracy":"93.95%"},"uses_additional_data":false,"paper_date":"2022-05-24","paper":"/paper/sface-sigmoid-constrained-hypersphere-loss-1","paper_url":"https://arxiv.org/abs/2205.12010v1","paper_title":"SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition","code":"https://github.com/serengil/deepface","n_code_links":6,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}