Deep face recognition: A survey
Mei Wang,Weihong Deng +1 more
TLDR
A comprehensive review of the recent developments on deep face recognition can be found in this paper, covering broad topics on algorithm designs, databases, protocols, and application scenes, as well as the technical challenges and several promising directions.About:
This article is published in Neurocomputing.The article was published on 2021-03-14 and is currently open access. It has received 353 citations till now. The article focuses on the topics: Deep learning & Feature extraction.read more
Citations
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3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size
TL;DR: This paper presents an expression-invariant 3D face recognition method based on transfer learning and Siamese networks that can resolve the small sample size issue and can be used for facial recognition with a single sample.
Proceedings ArticleDOI
A Survey on Loss Functions for Deep Face Recognition Network
Aly Khalifa,Ayoub Al-Hamadi +1 more
TL;DR: In this article, the authors compare the performance of different loss functions for deep face recognition networks and show that softmax loss does not have the sufficient discriminative power needed for face recognition.
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Rapid Face Image Retrieval for Large Scale Based on Spark and Machine Learning
TL;DR: Wang et al. as discussed by the authors proposed a distributed computing environment large-scale face image retrieval method based on machine learning, which takes advantage of parallel computing to improve the efficiency of face image image retrieval.
Proceedings ArticleDOI
A large-scale TV video and metadata database for French political content analysis and fact-checking
TL;DR: A large-scale multimodal publicly available dataset for the French political content analysis and fact-checking that consists of more than 1,200 fact-checked claims that have been scraped from a fact- checking service with associated metadata is introduced.
Journal ArticleDOI
Prototype Memory for Large-Scale Face Representation Learning
TL;DR: Prototype Memory as discussed by the authors uses a limited-size memory module for storing recent class prototypes and employs a set of algorithms to update it in appropriate way to prevent prototype obsolescence.
References
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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.
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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 ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
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Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.