One-to-many face recognition with bilinear CNNs

@article{Chowdhury2016OnetomanyFR,
  title={One-to-many face recognition with bilinear CNNs},
  author={Aruni Roy Chowdhury and Tsung-Yu Lin and Subhransu Maji and Erik G. Learned-Miller},
  journal={2016 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2016},
  pages={1-9}
}
The recent explosive growth in convolutional neural network (CNN) research has produced a variety of new architectures for deep learning. [] Key Result This B-CNN improves upon the CNN performance on the IJB-A benchmark, achieving 89.5% rank-1 recall.

Figures and Tables from this paper

Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks
TLDR
The design details of a deep learning system for unconstrained face recognition, including modules for face detection, association, alignment and face verification are presented and some open issues regarding DCNNs for face verification problems are discussed.
Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition
TLDR
This paper proposes a new loss function, called Crystal Loss, that restricts the features to lie on a hypersphere of a fixed radius and shows that integrating this simple step in the training pipeline significantly improves the performance of face verification and recognition systems.
A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion
TLDR
This paper proposes a unified learning framework named Transferred Deep Feature Fusion (TDFF) targeting at the new IARPA Janus Benchmark A (IJB-A) face recognition dataset released by NIST face challenge, and exhibits excellent performance on IJB-A dataset.
Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans
TLDR
An overview of deep-learning methods used for face recognition is provided and different modules involved in designing an automatic face recognition system are discussed and the role of deep learning for each of them is discussed.
L2-constrained Softmax Loss for Discriminative Face Verification
TLDR
This paper adds an L2-constraint to the feature descriptors which restricts them to lie on a hypersphere of a fixed radius and shows that integrating this simple step in the training pipeline significantly boosts the performance of face verification.
Factorized Bilinear Models for Image Recognition
TLDR
A novel Factorized Bilinear (FB) layer is proposed to model the pairwise feature interactions by considering the quadratic terms in the transformations of CNNs to reduce the risk of overfitting.
Effective Methods for Lightweight Image-Based and Video-Based Face Recognition
  • Yi-Wei Ma
  • Computer Science
    2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
  • 2019
TLDR
A scaling method on MobileFaceNet to boost the performance with the limit of computational cost, and a simple supplementary method for average pooling which throws up those noise frames based on the cluster information in video face recognition are proposed.
Deep Face Recognition: A Survey
A Fast and Accurate System for Face Detection, Identification, and Verification
TLDR
A novel face detector, deep pyramid single shot face detector (DPSSD), which is fast and detects faces with large scale variations (especially tiny faces), and a new loss function, called crystal loss, for the tasks of face verification and identification.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 31 REFERENCES
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.
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.
Bilinear CNN Models for Fine-Grained Visual Recognition
We propose bilinear models, a recognition architecture that consists of two feature extractors whose outputs are multiplied using outer product at each location of the image and pooled to obtain an
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.
Face recognition in unconstrained videos with matched background similarity
TLDR
A comprehensive database of labeled videos of faces in challenging, uncontrolled conditions, the ‘YouTube Faces’ database, along with benchmark, pair-matching tests are presented and a novel set-to-set similarity measure, the Matched Background Similarity (MBGS), is described.
Very Deep Convolutional Networks for Large-Scale Image Recognition
TLDR
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
ImageNet classification with deep convolutional neural networks
TLDR
A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Deep Learning Face Representation by Joint Identification-Verification
TLDR
This paper shows that the face identification-verification task can be well solved with deep learning and using both face identification and verification signals as supervision, and the error rate has been significantly reduced.
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.
Unconstrained face verification using deep CNN features
TLDR
An algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset as well as on the traditional Labeled Face in the Wild (LFW) dataset.
...
1
2
3
4
...