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Deep face recognition: A survey

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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.
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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.

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Adversarial Learning with Margin-based Triplet Embedding Regularization.

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CNN Inference acceleration using low-power devices for human monitoring and security scenarios

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A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education

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Deep-learned faces: a survey

TL;DR: This paper first discusses the evolution of related network architectures, then a comparative analysis of loss functions, classification algorithms, and face datasets is given and a comparative study of state-of-the-art face recognition systems is presented.
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Deep softmax collaborative representation for robust degraded face recognition

TL;DR: This work proposes a deep softmax collaborative representation-based network, which can be used as a divide-and-conquer algorithm to help multiple DCCNs work together more effectively to solve multiple sub-problems of face reconstruction and classification.
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

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

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

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.
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