Gabor filter bank with deep autoencoder based face recognition system

•We propose an efficient face recognition system based on Gabor filter bank and SAE.•Gabor filters have not been adequately coupled with deep learning schemes.•Sparse Auto-Encoder ameliorates the features generated by Gabor filters.•The proposed system outperforms existing methods on seven popular f...

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Vydáno v:Expert systems with applications Ročník 197; s. 116743
Hlavní autoři: Hammouche, Rabah, Attia, Abdelouahab, Akhrouf, Samir, Akhtar, Zahid
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.07.2022
Elsevier BV
Témata:
Discriminant analysis
Electronic banking
Face recognition
Feature extraction
Filter banks
Gabor filter bank
Gabor filters
Machine learning
PCA+LDA
Principal components analysis
Sparse AutoEncoder
Gabor filter bank
PCA+LDA
Face recognition
Sparse AutoEncoder
ISSN:0957-4174, 1873-6793
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Shrnutí:•We propose an efficient face recognition system based on Gabor filter bank and SAE.•Gabor filters have not been adequately coupled with deep learning schemes.•Sparse Auto-Encoder ameliorates the features generated by Gabor filters.•The proposed system outperforms existing methods on seven popular face databases.•The proposed system can achieve optimal results. These days, face recognition systems are widely being employed in various daily applications such as smart phone unlocking, tracking school attendance, and secure online bank transactions, smarter border control, to name a few. In spite of the remarkable progress, face recognition systems still suffer from occlusions, light variations, camera types and their resolutions. Face recognition is still a dynamic research field. In this paper, we propose an efficient face recognition system based on Gabor filter bank and a deep learning method known as Sparse AutoEncoder (SAE). The main aim of the proposed system is to improve the features extracted by Gabor filter bank using SAE method. Then, these enhanced features are subjected to features reduction using principal component analysis and linear discriminant analysis (PCA + LDA) technique. Finally, the matching stage is accomplished via cosine Mahalanobis distance. Experiments on seven publicly available databases (i.e., JAFFE, AT&T, Yale, Georgia Tech, CASIA, Extended Yale, Essex) show that the proposed system can achieve promising results with the combination of Gabor and SAE, as well as outperform previously proposed methods.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2022.116743