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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Book ChapterDOI
A Capsule Network for Hierarchical Multi-label Image Classification
TL;DR: In this article , a multi-label capsule network (ML-CapsNet) is proposed to predict multiple image classes based on a hierarchical class-label tree structure and a loss function that takes into account the multilabel predictions of the network.
Book ChapterDOI
A Novel Hierarchical Face Recognition Method Based on the Geometrical Face Features and Convolutional Neural Network with a New Layer Arrangement
TL;DR: In this article , the authors used the active appearance graph model (AMM) as the first feature extractor and CNN as the second extractor for feature extraction and classification, and investigated the results of two different types of classifiers, SVM and Softmax.
Proceedings ArticleDOI
Request-Aware Task Offloading in Mobile Edge Computing via Deep Reinforcement Learning
TL;DR: In this paper , a request aware task offloading (RATO) scheme was proposed aiming at the problem that the limited edge server computing resources made it impossible to meet the requirements of task completion delay and device energy consumption with the optimization objective to minimize the weighted total overhead.
Journal ArticleDOI
A Face Recognition Algorithm Based on Improved Resnet
TL;DR: Experiments show that an improved loss function algorithm based on the Resnet-50 model can not only learn deep-face characteristics but also efficiently improve the accuracy of face recognition compared with ordinary CNN.
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.