UMDFaces: An annotated face dataset for training deep networks

@article{Bansal2017UMDFacesAA,
  title={UMDFaces: An annotated face dataset for training deep networks},
  author={A. Bansal and Anirudh Nanduri and C. Castillo and Rajeev Ranjan and R. Chellappa},
  journal={2017 IEEE International Joint Conference on Biometrics (IJCB)},
  year={2017},
  pages={464-473}
}
Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets. [...] Key Method We discuss how a large dataset can be collected and annotated using human annotators and deep networks. We provide human curated bounding boxes for faces. We also provide estimated pose (roll, pitch and yaw), locations of twenty-one key-points and gender information generated by a pre-trained neural network…Expand
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References

SHOWING 1-10 OF 50 REFERENCES
WIDER FACE: A Face Detection Benchmark
TLDR
There is a gap between current face detection performance and the real world requirements, and the WIDER FACE dataset, which is 10 times larger than existing datasets is introduced, which contains rich annotations, including occlusions, poses, event categories, and face bounding boxes. Expand
Deep Learning Face Attributes in the Wild
TLDR
A novel deep learning framework for attribute prediction in the wild that cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. Expand
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
TLDR
This work revisits both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. Expand
Deep Face Recognition
TLDR
It is shown how a very large scale dataset can be assembled by a combination of automation and human in the loop, and the trade off between data purity and time is discussed. Expand
Web-scale training for face identification
TLDR
A link between the representation norm and the ability to discriminate in a target domain is found, which sheds lights on how deep convolutional networks represent faces. Expand
Learning Face Representation from Scratch
TLDR
A semi-automatical way to collect face images from Internet is proposed and a large scale dataset containing about 10,000 subjects and 500,000 images, called CASIAWebFace is built, based on which a 11-layer CNN is used to learn discriminative representation and obtain state-of-theart accuracy on LFW and YTF. Expand
Real-Time Facial Segmentation and Performance Capture from RGB Input
TLDR
A state-of-the-art regression-based facial tracking framework with segmented face images as training is adopted, and accurate and uninterrupted facial performance capture is demonstrated in the presence of extreme occlusion and even side views. Expand
Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A
TLDR
Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark. Expand
HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition
TLDR
The proposed method, HyperFace, fuses the intermediate layers of a deep CNN using a separate CNN followed by a multi-task learning algorithm that operates on the fused features to exploit the synergy among the tasks which boosts up their individual performances. Expand
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
TLDR
A benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data, which could lead to one of the largest classification problems in computer vision. Expand
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5
...