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Priming Deep Neural Networks with Synthetic Faces for Enhanced Performance., , , , , , , and . CoRR, (2018)Analyzing and Reducing the Damage of Dataset Bias to Face Recognition With Synthetic Data., , , , , and . CVPR Workshops, page 2261-2268. Computer Vision Foundation / IEEE, (2019)Spatially Varying Registration Using Gaussian Processes., , , , and . MICCAI (2), volume 8674 of Lecture Notes in Computer Science, page 413-420. Springer, (2014)Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems., , , , , and . CVPR Workshops, page 2093-2102. Computer Vision Foundation / IEEE Computer Society, (2018)Training Deep Face Recognition Systems with Synthetic Data., , , , , and . CoRR, (2018)Morphable Face Models - An Open Framework., , , , , , and . CoRR, (2017)Probabilistic Fitting of Active Shape Models., , , and . ShapeMI@@MICCAI, volume 11167 of Lecture Notes in Computer Science, page 137-146. Springer, (2018)Morphable Face Models - An Open Framework., , , , , , and . FG, page 75-82. IEEE Computer Society, (2018)Computations for constrained linear models, and . Journal of Econometrics, 12 (1): 59--84 (January 1980)On a further characterization of dispersion matrices based on the properties of regression, and . Journal of Statistical Planning and Inference, 10 (3): 265--271 (October 1984)