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
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Harnessing Unrecognizable Faces for Face Recognition.
TL;DR: In this paper, the authors propose a measure of recognizability of a face image that leverages a key empirical observation: an embedding of face images, implemented by a deep neural network trained using mostly recognizable identities, induces a partition of the hypersphere whereby unrecognizable identities cluster together.
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Computational Cartographic Recognition: Identifying Maps, Geographic Regions, and Projections from Images Using Machine Learning
Jialin Li,Ningchuan Xiao +1 more
TL;DR: In this paper , four machine learning models (support vector machine, multilayer perceptrons, convolutional neural networks (CNNs), and pretrained CNN models through transfer learning (CNNT) are applied to map reading.
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Reivew of Light Field Image Super-Resolution
TL;DR: In this paper , the authors outlined and discussed the current research on super-resolution light field images, including traditional methods and deep-learning-based methods, and compared the performance of various methods on these datasets as well as analyzed the importance of light field super resolution and its future development.
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Brain inspired face recognition: A computational framework
TL;DR: In this paper , the authors proposed a new computational model of face recognition which uses cues from the distributed face recognition mechanism of the brain, and by gathering engineering equivalent of these cues from existing literature.
Proceedings ArticleDOI
Deep Learning Based Emotion Detection in an Online Class
TL;DR: In this article , the authors used deep learning technology to detect real time emotions of students in online class using publically available dataset on Kaggle with the name JonathanOheix.
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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.