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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Journal ArticleDOI
Pengenalan ekspresi raut wajah berbasis jaringan saraf tiruan backpropagation dengan metode principal component analysis
TL;DR: The results of this study show that facial pattern Recognition based on the proportion of memorization and generalization for the use of the method without PCA is better than facial pattern recognition using PCA.
Book ChapterDOI
Photobook Creation Using Face Recognition and Machine Learning
TL;DR: In this article, face recognition and deep learning techniques are utilized for photobook creation, wherein the user can create albums for a particular group of people or individuals and annotate them.
Journal ArticleDOI
Privacy-Preserved In-Cabin Monitoring System for Autonomous Vehicles
TL;DR: This study proposes a privacy-preserved IMS, which can reidentify anonymized virtual individual faces in an abnormal situation and preserves personal privacy while maintaining security in surveillance systems, such as for in-cabin monitoring of FAVs.
Journal ArticleDOI
FVFSNet: Frequency-Spatial Coupling Network for Finger Vein Authentication
TL;DR: Wang et al. as mentioned in this paper proposed a novel frequency-spatial coupling network (FVFSNet) for finger vein authentication, which is mainly composed of three parts: (1) the frequency domain processing module (FDPM), (2) the spatial domain Processing module (SDPM), and (3) the Frequency-Spatial coupling module (FSCM).
Journal ArticleDOI
Face Image Analysis Using Machine Learning: A Survey on Recent Trends and Applications
TL;DR: This survey paper presents a comprehensive review focusing on methods in both controlled and uncontrolled conditions, starting from seminal works on face image analysis and ending with the latest ideas exploiting deep learning frameworks.
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.