It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
In the past ten years, research on face recognition has shifted to using 3D facial surfaces, as 3D geometric information provides more discriminative features. This comprehensive survey reviews 3D face recognition techniques developed in the past decade, both conventional methods and deep learning methods. These methods are evaluated with detailed descriptions of selected representative works. Their advantages and disadvantages are summarized in terms of accuracy, complexity, and robustness to facial variations (expression, pose, occlusion, etc.). A review of 3D face databases is also provided, and a discussion of future research challenges and directions of the topic.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 Deakin University, The School of Information Technology, Waurn Ponds, Australia (GRID:grid.1021.2) (ISNI:0000 0001 0526 7079)