{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/paper/pose-robust-face-recognition-via-deep","title":"Pose-Robust Face Recognition via Deep Residual Equivariant Mapping","arxiv_id":"1803.00839","date":"2018-03-02","proceeding":"CVPR 2018 6","authors":["Kaidi Cao","Yu Rong","Cheng Li","Xiaoou Tang","Chen Change Loy"],"abstract":"Face recognition achieves exceptional success thanks to the emergence of deep\nlearning. However, many contemporary face recognition models still perform\nrelatively poor in processing profile faces compared to frontal faces. A key\nreason is that the number of frontal and profile training faces are highly\nimbalanced - there are extensively more frontal training samples compared to\nprofile ones. In addition, it is intrinsically hard to learn a deep\nrepresentation that is geometrically invariant to large pose variations. In\nthis study, we hypothesize that there is an inherent mapping between frontal\nand profile faces, and consequently, their discrepancy in the deep\nrepresentation space can be bridged by an equivariant mapping. To exploit this\nmapping, we formulate a novel Deep Residual EquivAriant Mapping (DREAM) block,\nwhich is capable of adaptively adding residuals to the input deep\nrepresentation to transform a profile face representation to a canonical pose\nthat simplifies recognition. The DREAM block consistently enhances the\nperformance of profile face recognition for many strong deep networks,\nincluding ResNet models, without deliberately augmenting training data of\nprofile faces. The block is easy to use, light-weight, and can be implemented\nwith a negligible computational overhead.","url_abs":"http://arxiv.org/abs/1803.00839v1","url_pdf":"http://arxiv.org/pdf/1803.00839v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pose-robust-face-recognition-via-deep","repo_url":"https://github.com/penincillin/DREAM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-2-Clause"}}],"tasks":[{"task_slug":"face-identification","task_name":"Face Identification"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"face-verification","task_name":"Face Verification"},{"task_slug":"robust-face-recognition","task_name":"Robust Face Recognition"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-identification-on-ijb-a","task":"Face Identification","dataset":"IJB-A","model":"Deep Residual Equivariant Mapping","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy":"94.60%"},"uses_additional_data":false},{"leaderboard":"/sota/face-verification-on-ijb-a","task":"Face Verification","dataset":"IJB-A","model":"Deep Residual Equivariant Mapping","rank_in_archive_order":7,"of":17,"metrics":{"TAR @ FAR=0.01":"94.40%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.00839","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.00839"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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