Book ChapterDOI
Evolution of Newborn Face Recognition
Pavani Tripathi,Rohit Keshari,Mayank Vatsa,Richa Singh +3 more
- pp 167-187
TLDR
In this article, the authors discuss various biometric modalities along with their advantages and limitations and present all the datasets available and existing hand-crafted, learning-based, as well as deep-learning-based techniques which have been proposed for new born face recognition.Abstract:
Accidental new born swapping, health-care tracking, and child-abduction cases are some of the scenarios where new born face recognition can prove to be extremely useful. With the help of the right biometric system in place, cases of swapping, for instance, can be evaluated much faster. In this chapter, we first discuss the various biometric modalities along with their advantages and limitations. We next discuss the face biometrics in detail and present all the datasets available and existing hand-crafted, learning-based, as well as deep-learning-based techniques which have been proposed for new born face recognition. Finally, we evaluate and compare these techniques. Our comparative analysis shows that the state-of-the-art SSF-CNN technique achieves an average of rank-1 new born accuracy of \(82.075\%\).read more
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: 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.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Book ChapterDOI
SURF: speeded up robust features
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
TL;DR: This work proposes a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit and derives a robust initialization method that particularly considers the rectifier nonlinearities.