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ArcFace: Additive Angular Margin Loss for Deep Face Recognition
This paper presents arguably the most extensive experimental evaluation against all recent state-of-the-art face recognition methods on ten face recognition benchmarks, and shows that ArcFace consistently outperforms the state of the art and can be easily implemented with negligible computational overhead.
300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge
The main goal of this challenge is to compare the performance of different methods on a new-collected dataset using the same evaluation protocol and the same mark-up and hence to develop the first standardized benchmark for facial landmark localization.
AgeDB: The First Manually Collected, In-the-Wild Age Database
This paper presents the first, to the best of knowledge, manually collected "in-the-wild" age database, dubbed AgeDB, containing images annotated with accurate to the year, noise-free labels, which renders AgeDB suitable when performing experiments on age-invariant face verification, age estimation and face age progression "in the wild".
Robust Discriminative Response Map Fitting with Constrained Local Models
We present a novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows
Incremental Face Alignment in the Wild
It is shown that it is possible to automatically construct robust discriminative person and imaging condition specific models 'in- the-wild' that outperform state-of-the-art generic face alignment strategies.
The First Facial Landmark Tracking in-the-Wild Challenge: Benchmark and Results
The first benchmark for long-term facial landmark tracking, containing currently over 110 annotated videos, is presented, and the results of the competition on facial landmark localisation in static imagery are summarized.
RetinaFace: Single-stage Dense Face Localisation in the Wild
A robust single-stage face detector, named RetinaFace, which performs pixel-wise face localisation on various scales of faces by taking advantages of joint extra-supervised and self-super supervised multi-task learning.
Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment
This paper proposes a combined and jointly trained convolutional recurrent neural network architecture that allows the training of an end-to-end to system that attempts to alleviate the drawbacks of cascaded regression.
300 Faces In-The-Wild Challenge: database and results
This paper proposes a semi-automatic annotation technique that was employed to re-annotate most existing facial databases under a unified protocol, and presents the 300 Faces In-The-Wild Challenge (300-W), the first facial landmark localization challenge that was organized twice, in 2013 and 2015.
A Semi-automatic Methodology for Facial Landmark Annotation
This is the first attempt to create a tool suitable for annotating massive facial databases, and the tool for creating annotations for MultiPIE, XM2VTS, AR, and FRGC Ver. 2 databases is employed.