3,806 research outputs found
Unsupervised Training for 3D Morphable Model Regression
We present a method for training a regression network from image pixels to 3D
morphable model coordinates using only unlabeled photographs. The training loss
is based on features from a facial recognition network, computed on-the-fly by
rendering the predicted faces with a differentiable renderer. To make training
from features feasible and avoid network fooling effects, we introduce three
objectives: a batch distribution loss that encourages the output distribution
to match the distribution of the morphable model, a loopback loss that ensures
the network can correctly reinterpret its own output, and a multi-view identity
loss that compares the features of the predicted 3D face and the input
photograph from multiple viewing angles. We train a regression network using
these objectives, a set of unlabeled photographs, and the morphable model
itself, and demonstrate state-of-the-art results.Comment: CVPR 2018 version with supplemental material
(http://openaccess.thecvf.com/content_cvpr_2018/html/Genova_Unsupervised_Training_for_CVPR_2018_paper.html
Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region classifiers
In this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D face classifier based on the fusion of many dependent region classifiers for overlapping face regions. The region classifiers use PCA-LDA for feature extraction and the likelihood ratio as a matching score. Fusion is realised using straightforward majority voting for the identification scenario. For verification, a voting approach is used as well and the decision is defined by comparing the number of votes to a threshold. Using the proposed registration method combined with a classifier consisting of 60 fused region classifiers we obtain a 99.0% identification rate on the all vs first identification test of the FRGC v2 data. A verification rate of 94.6% at FAR=0.1% was obtained for the all vs all verification test on the FRGC v2 data using fusion of 120 region classifiers. The first is the highest reported performance and the second is in the top-5 of best performing systems on these tests. In addition, our approach is much faster than other methods, taking only 2.5 seconds per image for registration and less than 0.1 ms per comparison. Because we apply feature extraction using PCA and LDA, the resulting template size is also very small: 6 kB for 60 region classifiers
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