Deep face recognition: A survey
Mei Wang,Weihong Deng +1 more
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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
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Journal ArticleDOI
Relational Deep Feature Learning for Heterogeneous Face Recognition
TL;DR: In this paper, a graph-structured module called Relational Graph Module (RGM) was proposed to extract global relational information in addition to general facial features, which can help cross-domain matching.
Posted Content
FaceHack: Triggering backdoored facial recognition systems using facial characteristics.
TL;DR: This work demonstrates that specific changes to facial characteristics may also be used to trigger malicious behavior in an ML model and substantiates the undetectability of the triggers by exhaustively testing them with state-of-the-art defenses.
Journal ArticleDOI
Human Face Detection Techniques: A Comprehensive Review and Future Research Directions
TL;DR: A comprehensive survey of face detection algorithms can be found in this article, where the authors explore a wide variety of the available face detection methods in five steps, including history, working procedure, advantages, limitations, and use in other fields alongside face detection.
Journal ArticleDOI
Unconstrained and constrained face recognition using dense local descriptor with ensemble framework
TL;DR: The templates of D-LGS are optimized using Genetic algorithm (GA) as part of ‘curse-of-dimensionality’ and the reduced number of templates give accuracies of 100% on AT&T and 99.2165% on LFW face database.
Journal ArticleDOI
Learning upper patch attention using dual-branch training strategy for masked face recognition
TL;DR: In this paper , a dual-branch training strategy was proposed to guide the model to focus on the upper half of the face to extract robust features for Masked face recognition.
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
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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).
Journal ArticleDOI
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.