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In order to address this limitation, this paper proposed a coarse-to-fine method only based on the 3D facial scan data extracted from professional equipment to automatically and accurately estimate the landmark localization. Specifically, we firstly trained a convolutional neural network (CNN) to initialize the face landmarks instead of the mean shape. Then the proposed cascade regression networks learn the mapping function between 3D facial geometry feature and landmarks location. Tested on Bosphorus database, the experimental results demonstrated effectiveness and accuracy of the proposed method for [Formula: see text] landmarks. Compared with other methods, the results in several points demonstrate state-of-the-art performance.<\/jats:p>","DOI":"10.1142\/s0219691318400076","type":"journal-article","created":{"date-parts":[[2018,1,4]],"date-time":"2018-01-04T09:22:32Z","timestamp":1515057752000},"page":"1840007","source":"Crossref","is-referenced-by-count":6,"title":["Accurate landmarking from 3D facial scans by CNN and cascade regression"],"prefix":"10.1142","volume":"16","author":[{"given":"Wang","family":"Kai","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"An","sequence":"additional","affiliation":[{"name":"School of Management, Northwestern Polytechnical University, Xi\u2019an 710072, P. R. 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