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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An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments
TL;DR: This paper is aimed at providing the audio recognition community with a carefully annotated dataset for FSL and OSR comprised of 1360 clips from 34 classes divided into pattern sounds and unwanted sounds.
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PF-cpGAN: Profile to Frontal Coupled GAN for Face Recognition in the Wild
TL;DR: This paper uses a coupled generative adversarial network structure (cpGAN) structure to find the hidden relationship between the profile and frontal images in a latent common embedding subspace and exploits this connection by projecting the profile faces and frontal faces into a common latent space and perform verification or retrieval in the latent domain.
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PropagationNet: Propagate Points to Curve to Learn Structure Information
TL;DR: This paper presents a novel structure-infused face alignment algorithm based on heatmap regression via propagating landmark heatmaps to boundary heatmaps, which provide structure information for further attention map generation and adopts methods from other fields that address the shift variance problem of CNN for face alignment.
Journal ArticleDOI
UAV surveillance for violence detection and individual identification
TL;DR: The concept of transfer learning using deep learning-based different hybrid models with LSTM for violence detection has been proposed and proved to be noteworthy and thereby, demonstrating its superiority over other models that have been tested.
Journal ArticleDOI
Deep learning for face recognition on mobile devices
TL;DR: In this work, a small-size deep-learning model was trained for face recognition on low capacity devices and evaluated in terms of accuracy, size and timings to provide quantitative data.
References
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
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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.