Proceedings ArticleDOI
IARPA Janus Benchmark - C: Face Dataset and Protocol
Brianna Maze,Jocelyn Adams,James A. Duncan,Nathan D. Kalka,Tim Miller,Charles Otto,Anil K. Jain,W. Tyler Niggel,Janet Anderson,Jordan Cheney,Patrick J. Grother +10 more
- pp 158-165
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TLDR
The IARPA Janus Benchmark–C (IJB-C) face dataset advances the goal of robust unconstrained face recognition, improving upon the previous public domain IJB-B dataset, by increasing dataset size and variability, and by introducing end-to-end protocols that more closely model operational face recognition use cases.Abstract:
Although considerable work has been done in recent years to drive the state of the art in facial recognition towards operation on fully unconstrained imagery, research has always been restricted by a lack of datasets in the public domain In addition, traditional biometrics experiments such as single image verification and closed set recognition do not adequately evaluate the ways in which unconstrained face recognition systems are used in practice The IARPA Janus Benchmark–C (IJB-C) face dataset advances the goal of robust unconstrained face recognition, improving upon the previous public domain IJB-B dataset, by increasing dataset size and variability, and by introducing end-to-end protocols that more closely model operational face recognition use cases IJB-C adds 1,661 new subjects to the 1,870 subjects released in IJB-B, with increased emphasis on occlusion and diversity of subject occupation and geographic origin with the goal of improving representation of the global population Annotations on IJB-C imagery have been expanded to allow for further covariate analysis, including a spatial occlusion grid to standardize analysis of occlusion Due to these enhancements, the IJB-C dataset is significantly more challenging than other datasets in the public domain and will advance the state of the art in unconstrained face recognitionread more
Citations
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Proceedings ArticleDOI
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
TL;DR: 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.
Proceedings ArticleDOI
VGGFace2: A Dataset for Recognising Faces across Pose and Age
TL;DR: VGGFace2 as discussed by the authors is a large-scale face dataset with 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject.
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ArcFace: Additive Angular Margin Loss for Deep Face Recognition
TL;DR: This article proposed an additive angular margin loss (ArcFace) to obtain highly discriminative features for face recognition, which has a clear geometric interpretation due to the exact correspondence to the geodesic distance on the hypersphere.
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FSGAN: Subject Agnostic Face Swapping and Reenactment
TL;DR: A novel recurrent neural network (RNN)-based approach for face reenactment which adjusts for both pose and expression variations and can be applied to a single image or a video sequence and uses a novel Poisson blending loss which combines Poisson optimization with perceptual loss.
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Circle Loss: A Unified Perspective of Pair Similarity Optimization
TL;DR: In this paper, the authors propose a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the betweenclass similarity$s_n$.
References
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Robust Real-Time Face Detection
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Proceedings ArticleDOI
Robust real-time face detection
Paul A. Viola,Michael Jones +1 more
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