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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Proceedings ArticleDOI
A Hierarchical Bayes-Based Evolutionary Ensemble Classification Algorithm
TL;DR: Huang et al. as discussed by the authors proposed a hierarchical Bayes-based evolutionary ensemble (HBEE) classification algorithm that computes and utilises their new data-driven posterior-based class similarity to evolve a tree of weak classifiers.
Journal ArticleDOI
Fast Search of Face Recognition Model for a Mobile Device based on Neural Architecture Comparator
TL;DR: In this paper , a fast neural network-based facial feature extractor for offline mobile applications is proposed. But, the proposed approach is limited to the LFW face recognition task.
Posted Content
Comparing Human and Machine Bias in Face Recognition.
Samuel Dooley,Ryan Downing,George Wei,Nathan Shankar,Bradon Thymes,Gudrun Thorkelsdottir,Tiye Kurtz-Miott,Rachel Mattson,Olufemi Obiwumi,Valeriia Cherepanova,Micah Goldblum,John P. Dickerson,Tom Goldstein +12 more
TL;DR: In this paper, a series of challenging facial identification and verification questions were administered to various algorithms and a large, balanced sample of human reviewers, and they found that both computer models and human survey participants perform significantly better at the verification task, generally obtaining lower accuracy rates on dark-skinned or female subjects for both tasks, and obtain higher accuracy rates when their demographics match that of the question.
Journal ArticleDOI
Integrating audio and visual modalities for multimodal personality trait recognition via hybrid deep learning
TL;DR: Wang et al. as mentioned in this paper proposed a new method of multimodal personality trait recognition integrating audio-visual modalities based on a hybrid deep learning framework, which is comprised of convolutional neural networks (CNN), bi-directional long short-term memory network (Bi-LSTM), and the Transformer network.
References
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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.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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Going deeper with convolutions
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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
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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.