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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE access Jg. 13; S. 195080 - 195103
Hauptverfasser:	Masoudi, Azin, Ahmadi, Majid
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!