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Iacopo Masi
Researcher at Information Sciences Institute
Publications - 62
Citations - 4462
Iacopo Masi is an academic researcher from Information Sciences Institute. The author has contributed to research in topics: Facial recognition system & Face (geometry). The author has an hindex of 26, co-authored 59 publications receiving 3423 citations. Previous affiliations of Iacopo Masi include Sapienza University of Rome & University of Florence.
Papers
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Proceedings ArticleDOI
Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network
TL;DR: This paper used a CNN to regress 3DMM shape and texture parameters directly from an input photo and achieved state-of-the-art results on the LFW, YTF and IJB-A benchmarks.
Proceedings ArticleDOI
Deep Face Recognition: A Survey
TL;DR: The survey provides a clear, structured presentation of the principal, state-of-the-art (SOTA) face recognition techniques appearing within the past five years in top computer vision venues with some open issues currently overlooked by the community.
Book ChapterDOI
Do We Really Need to Collect Millions of Faces for Effective Face Recognition
TL;DR: In this paper, the authors propose a domain specific data augmentation method to enrich an existing dataset with important facial appearance variations by manipulating the faces it contains, which is also used when matching query images represented by standard convolutional neural networks.
Journal ArticleDOI
Person Re-Identification by Iterative Re-Weighted Sparse Ranking
TL;DR: The approach makes use of soft- and hard- re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration of an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets.
Proceedings ArticleDOI
Pose-Aware Face Recognition in the Wild
TL;DR: A method to push the frontiers of unconstrained face recognition in the wild by using multiple pose specific models and rendered face images called Pose-Aware Models (PAMs), which achieve remarkably better performance than commercial products and surprisingly also outperform methods that are specifically fine-tuned on the target dataset.