{"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/supervised-transformer-network-for-efficient","title":"Supervised Transformer Network for Efficient Face Detection","arxiv_id":"1607.05477","date":"2016-07-19","proceeding":null,"authors":["Dong Chen","Gang Hua","Fang Wen","Jian Sun"],"abstract":"Large pose variations remain to be a challenge that confronts real-word face\ndetection. We propose a new cascaded Convolutional Neural Network, dubbed the\nname Supervised Transformer Network, to address this challenge. The first stage\nis a multi-task Region Proposal Network (RPN), which simultaneously predicts\ncandidate face regions along with associated facial landmarks. The candidate\nregions are then warped by mapping the detected facial landmarks to their\ncanonical positions to better normalize the face patterns. The second stage,\nwhich is a RCNN, then verifies if the warped candidate regions are valid faces\nor not. We conduct end-to-end learning of the cascaded network, including\noptimizing the canonical positions of the facial landmarks. This supervised\nlearning of the transformations automatically selects the best scale to\ndifferentiate face/non-face patterns. By combining feature maps from both\nstages of the network, we achieve state-of-the-art detection accuracies on\nseveral public benchmarks. For real-time performance, we run the cascaded\nnetwork only on regions of interests produced from a boosting cascade face\ndetector. Our detector runs at 30 FPS on a single CPU core for a VGA-resolution\nimage.","url_abs":"http://arxiv.org/abs/1607.05477v1","url_pdf":"http://arxiv.org/pdf/1607.05477v1.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":[],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"face-detection","task_name":"Face Detection"},{"task_slug":"region-proposal","task_name":"Region Proposal"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-detection-on-annotated-faces-in-the-wild","task":"Face Detection","dataset":"Annotated Faces in the Wild","model":"STN","rank_in_archive_order":5,"of":7,"metrics":{"AP":"0.9835"},"uses_additional_data":false},{"leaderboard":"/sota/face-detection-on-pascal-face","task":"Face Detection","dataset":"PASCAL Face","model":"STN","rank_in_archive_order":5,"of":6,"metrics":{"AP":"0.9410"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1607.05477","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}