Deep face recognition: A survey
Mei Wang,Weihong Deng +1 more
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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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Book ChapterDOI
Single View Facial Age Estimation Using Deep Learning with Cascaded Random Forests
TL;DR: In this paper, a two-stage supervised learning model utilizes ResNeXt as a backbone combined with a 2-layer random forest (TLRF) to estimate age using unconstrained facial images.
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Bandwidth Slicing to Boost Federated Learning in Edge Computing
TL;DR: Results reveal that bandwidth slicing significantly improves training efficiency while achieving good learning accuracy.
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Bias, awareness, and ignorance in deep-learning-based face recognition
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TL;DR: In this article, the authors explore an intuitive technique for reducing this bias, namely "blinding" models to sensitive features, such as gender or race, and show why this cannot be equated with reducing bias.
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VIsCUIT: Visual Auditor for Bias in CNN Image Classifier
TL;DR: VisCUIT as mentioned in this paper is an interactive visualization system that reveals how and why a CNN classifier is biased and helps users discover and characterize the cause of the underperformances by revealing image concepts responsible for activating neurons that contribute to misclassification.
Journal ArticleDOI
Interest points reduction using evolutionary algorithms and CBIR for face recognition
Juan Villegas-Cortez,César Benavides-Álvarez,Carlos Avilés-Cruz,Graciela Román-Alonso,Francisco Vega,Francisco Chávez,Salomón Cordero-Sánchez +6 more
TL;DR: An evolutionary computer genetic algorithm for optimizing the number of interest points on faces is implemented to get a quick and precise facial recognition using local analysis texture technique applied to CBIR methodology.
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