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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OPOM: Customized Invisible Cloak Towards Face Privacy Protection
TL;DR: Zhang et al. as mentioned in this paper proposed a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks by optimizing each training sample in the direction away from the feature subspace of the source identity.
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ResNet34 Derin Öğrenme Mimarisi Kullanılarak Yüz Görüntülerinden Vücut Ağırlığı Tahmini Uygulaması
TL;DR: In this paper, the authors present a model of the ResNet34 model with a modified version of the model Olusturulan ResNet 34 model and show that it performs well.
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Reconstruct face from features based on genetic algorithm using GAN generator as a distribution constraint
TL;DR: Zhang et al. as mentioned in this paper proposed the reconstruction of face images from deep features without accessing the CNN network configurations as a constrained optimization problem, which minimizes the distance between the features extracted from the original face image and the reconstructed face image.
Proceedings ArticleDOI
Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings
TL;DR: In this article , a new method based on Incremental Variable Elimination (IVE) is proposed to secure multiple soft-biometric attributes simultaneously, which can be used to identify and discard multiple softbiometric at-tributes contained in face embeddings.
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Learning Gait Representations with Noisy Multi-Task Learning
TL;DR: This work proposes DenseGait, the largest dataset for pretraining gait analysis systems containing 217 K anonymized tracklets, annotated automatically with 42 appearance attributes, and proposes GaitFormer, a transformer-based model that after pretraining in a multi-task fashion on Dense Gait, achieves 92.5% accuracy on CASIA-B and 85.33% on FVG, without utilizing any manually annotated data.
References
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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.
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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.
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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.