Auto-Encoders Derivatives on Different Occluded Face Images: Comprehensive Review and New Results

This paper presents a novel approach for improving occluded face recognition performance using a family of autoencoders (AE) architectures. The proposed structures include four stages: image preprocessing, feature extraction using autoencoder derivatives, classification via a convolutional neural ne...

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Vydané v:	IEEE access Ročník 13; s. 195080 - 195103
Hlavní autori:	Masoudi, Azin, Ahmadi, Majid
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Artificial neural networks Autoencoders Coders Convolutional autoencoder Convolutional codes Convolutional neural networks Datasets Deep learning Deepfakes Face recognition Feature extraction Image reconstruction occluded dataset Occlusion Performance evaluation Robustness self-supervised autoencoder smooth autoencoder Transformers
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
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