{"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/a-data-driven-approach-to-improve-3d-head","title":"A Data-Driven Approach to Improve 3D Head-Pose Estimation","arxiv_id":null,"date":"2022-01-01","proceeding":"ISVC 2022 1","authors":["Nima Aghli","Eraldo Ribeiro"],"abstract":"Head-pose estimation from images is an important research topic in computer vision. Its many applications include detecting focus of attention, tracking driver behavior, and human-computer interaction. Recent research on head-pose estimation has focused on developing models based on deep convolutional neural networks (CNNs). These models are trained using transfer-learning and image augmentation to achieve better initiation states and robustness against occlusions. However, methods that use transfer-learning networks are usually aimed at general image recognition and offer no in-depth study of transfer learning from more task-related networks. Additionally, for the head-pose estimation, robustness against heavy occlusion, and noise such as motion blur and low-brightness are vital. In this paper, we propose a new image-augmentation approach that significantly improves the estimation accuracy of the head-pose model. We also propose a task-related weight initialization to further improve the estimation accuracy by studying internal activations of models trained for face-related tasks such as face-recognition. We test our head-pose estimation model on three challenging test sets and achieve better results to state-of-the-art methods.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-030-90439-5_43","url_pdf":"https://link.springer.com/content/pdf/10.1007%2F978-3-030-90439-5.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":"face-recognition","task_name":"Face Recognition"},{"task_slug":"head-pose-estimation","task_name":"Head Pose Estimation"},{"task_slug":"image-augmentation","task_name":"Image Augmentation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/head-pose-estimation-on-aflw2000","task":"Head Pose Estimation","dataset":"AFLW2000","model":"DDD-Pose","rank_in_archive_order":13,"of":25,"metrics":{"MAE":"4.22"},"uses_additional_data":false},{"leaderboard":"/sota/head-pose-estimation-on-biwi","task":"Head Pose Estimation","dataset":"BIWI","model":"DDD-Pose","rank_in_archive_order":16,"of":29,"metrics":{"MAE (trained with BIWI data)":"2.80","MAE (trained with other data)":"4.52"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}