Towards 3D Face Recognition in the Real: A Registration-Free Approach Using Fine-Grained Matching of 3D Keypoint Descriptors

Huibin Li, Di Huang, Jean-Marie Morvan, Yunhong Wang, Liming Chen
<span title="2014-11-12">2014</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hfdglwo5wbbmta6wop52fam7a4" style="color: black;">International Journal of Computer Vision</a> </i> &nbsp;
Registration algorithms performed on point clouds or range images of face scans have been successfully used for automatic 3D face recognition under expression variations, but have rarely been investigated to solve pose changes and occlusions mainly since that the basic landmarks to initialize coarse alignment are not always available. Recently, local feature-based SIFT-like match-Communicated by C. Schnörr.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11263-014-0785-6">doi:10.1007/s11263-014-0785-6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3dd24ybd5fdivgxgi7yhl64oki">fatcat:3dd24ybd5fdivgxgi7yhl64oki</a> </span>
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