{"type":"ScholarlyArticle","title":"Nonlinear Hierarchical Part-Based Regression for Unconstrained Face Alignment","authors":[{"name":"Xiang Yu","slug":"xiang-yu","given":"Xiang","family":"Yu","affiliation":""},{"name":"Zhe L. Lin","slug":"zhe-l-lin","given":"Zhe L.","family":"Lin","affiliation":""},{"name":"Shaoting Zhang","slug":"shaoting-zhang","given":"Shaoting","family":"Zhang","affiliation":""},{"name":"Dimitris N. Metaxas","slug":"dimitris-n-metaxas","given":"Dimitris N.","family":"Metaxas","affiliation":""}],"abstract":"Non-linear regression is a fundamental and yet under-developing methodology in solving many problems in Artificial Intelligence. The canonical control and predictions mostly utilize linear models or multi-linear models. However, due to the high non-linearity of the systems, those linear prediction models cannot fully cover the complexity of the problems. In this paper, we propose a robust two-stage hierarchical regression approach, to solve a popular Human-Computer Interaction, the unconstrained face-in-the-wild keypoint detection problem for computers. The environment is the still images, videos and live camera streams from machine vision. We firstly propose a holistic regression model to initialize the face fiducial points under different head pose assumptions. Second, to reduce local shape variance, a hierarchical part-based regression method is further proposed to refine the global regression output. Experiments on several challenging faces-in-the-wild datasets demonstrate the consistently better accuracy of our method, when compared to the state-of-the-art.","doi":null,"url":"https://ijcai.org/proceedings/2016/385","pdf":"https://ijcai.org/proceedings/2016/0385.pdf","markdown":"https://ijcai.org/proceedings/2016/385.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/385","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":385,"pages":{"first":2711,"last":2716},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}